SAA-C03 Sample Questions Answers

The best option is to use the AWS Snow Family console to order several AWS Snowball Edge Storage Optimized devices and use the devices to transfer the data to Amazon S3. Snowball Edge is a petabyte-scale data transfer device that can help transfer large amounts of data securely and quickly. Using Snowball Edge can be the most cost-effective solution for transferring large amounts of data over long distances and can help meet the requirement of transferring 600 TB of data within two weeks.

To ensure that the Lambda function ingests all data in the future despite occasional network connectivity issues, the following actions should be taken:

• Create an Amazon Simple Queue Service (SQS) queue and subscribe it to the SNS topic. This allows for decoupling of the notification and processing, so that even if the processing Lambda function fails, the message remains in the queue for further processing later.
• Modify the Lambda function to read from the SQS queue instead of directly from SNS. This decoupling allows for retries and fault tolerance and ensures that all messages are processed by the Lambda function.

Amazon S3 File Gateway is a hybrid cloud storage service that enables on-premises applications to seamlessly use Amazon S3 cloud storage. It provides a file interface to Amazon S3 and supports SMB and NFS protocols. It also supports S3 Lifecycle policies that can automatically transition data from S3 Standard to S3 Glacier Deep Archive after a specified period of time. This solution will meet the requirements of increasing the company’s available storage space without losing low-latency access to the most recently accessed files and providing file lifecycle management to avoid future storage issues.

Key phrase in the Question is must scale read and write capacity. Aurora is only for Read. Amazon DynamoDB has two read/write capacity modes for processing reads and writes on your tables: On-demand Provisioned (default, free-tier eligible) https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/HowItWorks.ReadWriteCapacityMode.html

This solution satisfies the requirements in the following ways:

• AWS Backup will automatically take full backups of the DynamoDB table on the schedule defined in the backup plan (the first of each month).

• The lifecycle policy can transition backups to cold storage after 6 months, meeting that requirement.

• Setting a 7-year retention period in the backup plan will ensure each backup is retained for 7 years as required.

• AWS Backup manages the backup jobs and lifecycle policies, requiring no custom scripting or management.

This answer is correct because it provides a resilient and durable replacement for the on-premises file share that is compatible with Windows IIS web servers. Amazon FSx for Windows File Server is a fully managed service that provides shared file storage built on Windows Server. It supports the SMB protocol and integrates with Microsoft Active Directory, which enables seamless access and authentication for Windows-based applications. Amazon FSx for Windows File Server also offers the following benefits:

• Resilience: Amazon FSx for Windows File Server can be deployed in multiple Availability Zones, which provides high availability and failover protection. It also supports automatic backups and restores, as well as self-healing features that detect and correct issues.
• Durability: Amazon FSx for Windows File Server replicates data within and across Availability Zones, and stores data on highly durable storage devices. It also supports encryption at rest and in transit, as well as file access auditing and data deduplication.
• Performance: Amazon FSx for Windows File Server delivers consistent sub-millisecond latencies and high throughput for file operations. It also supports SSD storage, native Windows features such as Distributed File System (DFS) Namespaces and Replication, and user-driven performance scaling.

References:

• Amazon FSx for Windows File Server
• Using Microsoft Windows file shares

Create an Amazon Elastic File System (Amazon EFS) file system. Mount the EFS file system on all web servers. To meet the requirements of providing a shared file store for Linux-based web servers without making changes to the application, using an Amazon EFS file system is the best solution. Amazon EFS is a managed NFS file system service that provides shared access to files across multiple Linux-based instances, which makes it suitable for this use case. Amazon S3 is not ideal for this scenario since it is an object storage service and not a file system, and it requires additional tools or libraries to mount the S3 bucket as a file system. Amazon CloudFront can be used to improve content delivery performance but is not necessary for this requirement. Additionally, Amazon EBS volumes can only be mounted to one instance at a time, so it is not suitable for sharing files across multiple instances.

AWS Database Migration Service supports homogeneous migrations such as Oracle to Oracle, as well as heterogeneous migrations between different database platforms, such as Oracle or Microsoft SQL Server to Amazon Aurora. With AWS Database Migration Service, you can also continuously replicate data with low latency from any supported source to any supported target. For example, you can replicate from multiple sources to Amazon Simple Storage Service (Amazon S3) to build a highly available and scalable data lake solution. You can also consolidate databases into a petabyte-scale data warehouse by streaming data to Amazon Redshift. Learn more about the supported source and target databases. https://aws.amazon.com/dms/

 This option is the most efficient because it uses Amazon CloudFront, which is a web service that speeds up distribution of your static and dynamic web content, such as .html, .css, .js, and image files, to your users1. It also uses a CloudFront distribution for the static content, which reduces the load on the EC2 instances and improves the performance and availability of the website. It also uses an Auto Scaling group to launch new instances based on network traffic, which automatically adjusts the compute capacity of your EC2 instances based on load or a schedule2. This solution meets the requirement of ensuring that all the requests are processed successfully during events for the launch of new products. Option A is less efficient because it uses a CloudFront distribution for the dynamic content, which is not necessary as the dynamic content is already handled by the API on the EC2 instances. It also increases the number of EC2 instances to handle the increase in traffic, which could incur higher costs and complexity than using an Auto Scaling group. Option C is less efficient because it uses an Amazon ElastiCache instance in front of the ALB to reduce traffic for the API to handle, which is a way to provide a fully managed in-memory data store service that provides sub-millisecond latency for caching and data processing3. However, this could introduce additional complexity and latency, and does not scale automatically based on network traffic. Option D is less efficient because it uses an Amazon Simple Queue Service (Amazon SQS) queue to receive requests from the website for later processing by the EC2 instances, which is a way to send, store, and receive messages between software components at any volume. However, this does not provide a faster response time to the users as they have to wait for their requests to be processed by the EC2 instances.

This option is the most efficient because it uses a gateway VPC endpoint for Amazon S3, which provides reliable connectivity to Amazon S3 without requiring an internet gateway or a NAT device for the VPC1. A gateway VPC endpoint routes traffic from the VPC to Amazon S3 using a prefix list for the service and does not leave the AWS network2. This meets the requirement of not traversing the internet. Option A is less efficient because it uses a private hosted zone by using Amazon Route 53, which is a DNS service that allows you to create custom domain names for your resources within your VPC3. However, this does not provide connectivity to Amazon S3 without an internet gateway or a NAT device. Option C is less efficient because it uses a NAT gateway to access the S3 bucket, which is a highly available, managed Network Address Translation (NAT) service that enables instances in a private subnet to connect to the internet or other AWS services, but prevents the internet from initiating a connection with those instances4. However, this does not meet the requirement of not traversing the internet. Option D is less efficient because it uses an AWS Site-to-Site VPN connection between the VPC and the S3 bucket, which is a secure and encrypted network connection between your on-premises network and your VPC. However, this does not meet the requirement of not traversing the internet.

• Restricting inbound access to the web servers to only port 443, which is used for HTTPS traffic, and allowing access from any IP address (0.0.0.0/0), since the application is public and accessible for global customers.
• Restricting inbound access to the DB instance to only port 3306, which is used for MySQL traffic, and allowing access only from the security group of the web servers, which creates a secure connection between the two tiers and prevents unauthorized access to the database.
• Restricting outbound access to the minimum required for both tiers, which is not specified in the question but can be assumed to be similar to the inbound rules.

References:

• Security groups - Amazon Virtual Private Cloud
• 5 Best Practices for AWS Security Groups - DZone

https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/PointInTimeRecovery.html

" online across multiple AWS Regions at all times". Currently only Read Replica supports cross-regions , Multi-AZ does not support cross-region (it works only in same region) https://aws.amazon.com/about-aws/whats-new/2018/01/amazon-rds-read-replicas-now-support-multi-az-deployments/

https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/Aurora.AuroraMySQL.html

This option is the most efficient because it uses Amazon SQS, which is a fully managed message queuing service that lets you send, store, and receive messages between software components at any volume, without losing messages or requiring other services to be available1. It also uses an SQS message queue to asynchronously dispatch requests to the different application tiers, which decouples the image upload process from the thumbnail generation process and enables scalability and reliability. It also alerts the user through an application message that the image was received, which provides a faster response time to the user than waiting for the thumbnail generation to complete. Option A is less efficient because it uses a custom AWS Lambda function to generate the thumbnail and alert the user, which is a way to run code without provisioning or managing servers. However, this does not use an asynchronous dispatch mechanism to separate the image upload process from the thumbnail generation process. It also uses the image upload process as an event source to invoke the Lambda function, which could cause concurrency issues if there are many images uploaded at once. Option B is less efficient because it uses AWS Step Functions, which is a fully managed service that provides a graphical console to arrange and visualize the components of your application as a series of steps2. However, this does not use an asynchronous dispatch mechanism to separate the image upload process from the thumbnail generation process. It also uses Step Functions to handle the orchestration between the application tiers and alert the user when thumbnail generation is complete, which could introduce additional complexity and latency. Option D is less efficient because it uses Amazon SNS, which is a fully managed messaging service that enables you to send messages or notifications directly to users with SMS text messages or email3. However, this does not use an asynchronous dispatch mechanism to separate the image upload process from the thumbnail generation process. It also uses SNS notification topics and subscriptions to generate the thumbnail after the image upload is complete and message the user’s mobile app by way of a push notification after thumbnail generation is complete, which could introduce additional complexity and latency.

This option is the most operationally efficient because it uses AWS Transfer Family to create an FTP server that can store incoming files in Amazon S3 Standard12, which is a low-cost and highly available storage service. It also uses AWS Lambda to process the files and delete them after they are processed, which is a serverless and scalable solution that does not require any batch scheduling or infrastructure management. It also uses S3 event notifications to invoke the Lambda function when the files arrive, which enables near real-time processing of the incoming data files3. Option A is less efficient because it uses Amazon S3 Glacier Flexible Retrieval, which is a cold storage class that has higher retrieval costs and longer retrieval times than Amazon S3 Standard. It also uses EventBridge rules to invoke the job nightly, which does not meet the requirement of processing incoming data files as soon as possible. Option B is less efficient because it uses an EBS volume to store incoming files, which is a block storage service that has higher costs and lower durability than Amazon S3. It also uses EventBridge rules to invoke the job nightly, which does not meet the requirement of processing incoming data files as soon as possible. Option C is less efficient because it uses an EBS volume to store incoming files, which is a block storage service that has higher costs and lower durability than Amazon S3. It also uses AWS Batch to process the files, which requires managing compute resources and job queues.

To ensure that Amazon S3 buckets do not have unauthorized configuration changes, a solutions architect should turn on AWS Config with the appropriate rules. AWS Config is a service that allows users to audit and assess their AWS resource configurations for compliance with industry standards and internal policies. It provides a detailed view of the resources and their configurations, including information on how the resources are related to each other. By turning on AWS Config with the appropriate rules, users can identify and remediate unauthorized configuration changes to their Amazon S3 buckets.

the best solution is to implement Amazon ElastiCache to cache the large datasets, which will store the frequently accessed data in memory, allowing for faster retrieval times. This can help to alleviate the frequent calls to the database, reduce latency, and improve the overall performance of the backend tier.

Amazon SES is a cost-effective and scalable email service that enables businesses to send and receive email using their own email addresses and domains. Configuring the web instance to send email through Amazon SES is a simple and effective solution that can reduce the time spent resolving complex email delivery issues and minimize operational overhead.

Windows file server is on-premise and we need something to replicate the data to the cloud, the only option we have is AWS FSx for Windows File Server. Also, since the information is confidential and sensitive, we also want to make sure that the appropriate users have access to it in a secure manner. https://docs.aws.amazon.com/fsx/ latest/WindowsGuide/what-is.html

This option is the most efficient because it sets an overall password policy for the entire AWS account, which is a way to specify complexity requirements and mandatory rotation periods for IAM user passwords1. It also meets the requirement of setting a password policy for all new users, as the password policy applies to all IAM users in the account. This solution meets the requirement of setting specific complexity requirements and mandatory rotation periods for IAM user passwords. Option B is less efficient because it sets a password policy for each IAM user in the AWS account, which is not possible as password policies can only be set at the account level. Option C is less efficient because it uses third-party vendor software to set password requirements, which is not necessary as IAM provides a built-in way to set password policies. Option D is less efficient because it attaches an Amazon CloudWatch rule to the Create_newuser event to set the password with the appropriate requirements, which is not possible as CloudWatch rules cannot modify IAM user passwords.

Amazon S3 sends event notifications about S3 buckets (for example, object created, object removed, or object restored) to an SNS topic in the same Region.

The SNS topic publishes the event to an SQS queue in the central Region.

The SQS queue is configured as the event source for your Lambda function and buffers the event messages for the Lambda function.

The Lambda function polls the SQS queue for messages and processes the Amazon S3 event notifications according to your application’s requirements.

https://docs.aws.amazon.com/prescriptive-guidance/latest/patterns/subscribe-a- lambda-function-to-event-notifications-from-s3-buckets-in-different-aws-regions.html

EFS is a standard file system, it scales automatically and is highly available.

1) SQS FIFO queues guarantee that messages are received in the exact order they are sent. Using the payment ID as the message group ensures all messages for a payment ID are received sequentially. 2) Kinesis data streams can also enforce ordering on a per partition key basis. Using the payment ID as the partition key will ensure strict ordering of messages for each payment ID.

Since the application has no local data on instances, AMIs alone can meet the RPO by restoring instances from the most recent AMI backup. When combined with automated RDS backups for the database, this provides a complete backup solution for this environment. The other options involving EBS snapshots would be unnecessary given the stateless nature of the instances. AMIs provide all the backup needed for the app tier. This uses native, automated AWS backup features that require minimal ongoing management: - AMI automated backups provide point-in-time recovery for the stateless app tier. - RDS automated backups provide point-in-time recovery for the database.

This solution will allow the company to host all three tiers on Amazon EC2 instances while using Amazon FSx for Windows File Server to provide Windows-based file sharing between the tiers. This will allow the company to use specific features of SQL Server, such as native backups and Data Quality Services, while sharing files for processing between the tiers.

Amazon Aurora Serverless for MySQL is a fully managed, auto-scaling relational database service that scales up or down automatically based on the application demand. This service provides all the capabilities of Amazon Aurora, such as high availability, durability, and security, without requiring the customer to provision any database instances. With Amazon Aurora Serverless for MySQL, the sales team can enjoy minimal downtime since the database is designed to automatically scale to accommodate the increased traffic. Additionally, the service allows the customer to pay only for the capacity used, making it cost-effective for infrequent access patterns. Amazon RDS for MySQL could also be an option, but it requires the customer to select an instance type, and the database administrator would need to monitor and adjust the instance size manually to accommodate the increasing traffic.

Dynamic scaling is a type of autoscaling that automatically adjusts the number of EC2 instances in an Auto Scaling group based on demand or load. It uses CloudWatch alarms to trigger scaling actions when a specified metric crosses a threshold. It can scale out (add instances) or scale in (remove instances) as needed1. By using dynamic scaling, the solution can maintain application performance during sudden traffic increases most cost-effectively.

A. Use manual scaling to change the size of the Auto Scaling group. This solution will not meet the requirement of maintaining application performance during sudden traffic increases, as manual scaling requires users to manually increase or decrease the number of instances through a CLI or console. It does not respond automatically to changes in demand or load2.

B. Use predictive scaling to change the size of the Auto Scaling group. This solution will not meet the requirement of most cost-effectiveness, as predictive scaling uses machine learning and artificial intelligence tools to evaluate traffic loads and anticipate when more or fewer resources are needed. It performs scheduled scaling actions based on the prediction, which may not match the actual demand or load at any given time. Predictive scaling is more suitable for scenarios where there are predictable traffic patterns or known changes in traffic loads3.

D. Use schedule scaling to change the size of the Auto Scaling group. This solution will not meet the requirement of maintaining application performance during sudden traffic increases, as schedule scaling performs scaling actions at specific times that users schedule. It does not respond automatically to changes in demand or load. Schedule scaling is more suitable for scenarios where there are predictable traffic drops or spikes at specific times of the day.

Reference URL: https://docs.aws.amazon.com/autoscaling/ec2/userguide/as-scale-based-on-demand.html

"RESTful web services" => API Gateway.

"EC2 instance receives the raw data, transforms the raw data, and stores all the data in an Amazon S3 bucket" => GLUE with (Extract - Transform - Load)

AWS Storage Gateway Volume Gateway provides two configurations for connecting to iSCSI storage, namely, stored volumes and cached volumes. The stored volume configuration stores the entire data set on-premises and asynchronously backs up the data to AWS. The cached volume configuration stores recently accessed data on-premises, and the remaining data is stored in Amazon S3. Since the company wants only its recently accessed data to remain stored locally, the cached volume configuration would be the most appropriate. It allows the company to keep frequently accessed data on-premises and reduce the need for scaling its iSCSI storage while still providing access to all data through the AWS cloud. This configuration also provides low-latency access to frequently accessed data and cost-effective off-site backups for less frequently accessed data.

https://docs.amazonaws.cn/en_us/storagegateway/latest/vgw/StorageGatewayConcepts.html#storage-gateway-cached-concepts

Amazon DynamoDB Time to Live (TTL) allows you to define a per-item timestamp to determine when an item is no longer needed. Shortly after the date and time of the specified timestamp, DynamoDB deletes the item from your table without consuming any write throughput. TTL is provided at no extra cost as a means to reduce stored data volumes by retaining only the items that remain current for your workload’s needs.

TTL is useful if you store items that lose relevance after a specific time. The following are example TTL use cases:

Remove user or sensor data after one year of inactivity in an application.

Archive expired items to an Amazon S3 data lake via Amazon DynamoDB Streams and AWS Lambda.

Retain sensitive data for a certain amount of time according to contractual or regulatory obligations.

https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/ TTL.html

This answer is correct because it allows the application to access Amazon S3 by using an IAM role that is associated with the ECS task. The task role grants permissions to the containers running in the task, and can be used to make AWS API calls from the application code. The taskRoleArn is a parameter in the task definition that specifies the IAM role to use for the task.

References:

• https://docs.aws.amazon.com/AmazonECS/latest/developerguide/task-iam-roles.html
• https://docs.aws.amazon.com/AmazonECS/latest/APIReference/API_TaskDefinition.html

To use AWS Budgets to create and manage budgets for different AWS accounts, the company needs to do the following steps:

• Use AWS Budgets to create a budget for each AWS account that needs a different budget amount. The budget can be based on cost or usage metrics, and can have different time periods, filters, and thresholds. The company can set the budget amount under the Billing dashboards of the required AWS accounts1.
• Create an IAM role for AWS Budgets to run budget actions with the required permissions. A budget action is a response that AWS Budgets initiates when a budget exceeds a specified threshold. The IAM role allows AWS Budgets to perform actions on behalf of the company, such as applying an IAM policy or a service control policy (SCP) to restrict the provisioning of additional resources2.
• Add an alert to notify the company when each account meets its budget threshold. The alert can be sent via email or Amazon SNS. The company can also add a budget action that selects the IAM role created and the appropriate SCP to prevent provisioning of additional resources. An SCP is a type of policy that can be applied to an AWS account or an organizational unit (OU) within AWS Organizations. An SCP can limit the actions that users and roles can perform in the account or OU3.

References:

• 4: https://aws.amazon.com/budgets/
• 1: https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/budgets-create.html
• 2: https://docs.aws.amazon.com/cost-management/latest/userguide/budgets-controls.html
• 3: https://docs.aws.amazon.com/organizations/latest/userguide/orgs_manage_policies_scps.html

This JSON text is an identity-based policy that grants specific permissions. The IAM principals that the solutions architect can attach this policy to are Role and Group. This is because the policy is written in JSON and is an identity-based policy, which can be attached to IAM principals such as users, groups, and roles. Identity-based policies are permissions policies that you attach to IAM identities (users, groups, or roles) and explicitly state what that identity is allowed (or denied) to do1. Identity-based policies are different from resource-based policies, which define the permissions around the specific resource1. Resource-based policies are attached to a resource, such as an Amazon S3 bucket or an Amazon EC2 instance1. Resource-based policies can also specify a principal, which is the entity that is allowed or denied access to the resource1. Organization is not an IAM principal, but a feature of AWS Organizations that allows you to manage multiple AWS accounts centrally2. Amazon ECS resource and Amazon EC2 resource are not IAM principals, but AWS resources that can have resource-based policies attached to them34.

References:

• Identity-based policies and resource-based policies
• AWS Organizations
• Amazon ECS task role
• Amazon EC2 instance profile

By purchasing a Compute Savings Plan, the company can save on the costs of running both EC2 instances and Lambda functions. The Lambda functions can be connected to the private subnet that contains the EC2 instances through a VPC endpoint for AWS services or a VPC peering connection. This provides direct network access to the EC2 instances while keeping the traffic within the private network, which helps to minimize network latency. Optimizing the Lambda functions’ duration, memory usage, number of invocations, and amount of data transferred can help to further minimize costs and improve performance. Additionally, using a private subnet helps to ensure that the EC2 instances are not directly accessible from the public internet, which is a security best practice.

Move the data objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 30 days - will meet the requirements of keeping the data immediately accessible with high availability and resiliency, while minimizing storage costs. S3 Standard-IA is designed for infrequently accessed data, and it provides a lower storage cost than S3 Standard, while still offering the same low latency, high throughput, and high durability as S3 Standard.

C and E are the correct answers because they allow the company to create a public endpoint for its web application that supports session affinity (sticky sessions) and has a WAF applied for additional security. By creating a public Application Load Balancer, the company can distribute incoming traffic across multiple EC2 instances in an Auto Scaling group and specify the application target group. By creating a web ACL in AWS WAF and associating it with the Application Load Balancer, the company can protect its web application from common web exploits. By enabling session stickiness on the Application Load Balancer, the company can ensure that subsequent requests from a user during a session are routed to the same target. References:

• Application Load Balancers
• AWS WAF
• Target Groups for Your Application Load Balancers
• How Application Load Balancer Works with Sticky Sessions

A -> We can configure CloudFront to require HTTPS from clients (enhanced security) https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/using-https-viewers-to-cloudfront.html D - > storing static website on S3 provides scalability and less operational overhead, then configuration of Application LB and EC2 instances (hence E is out)

**AWS PrivateLink provides private connectivity between VPCs, AWS services, and your on-premises networks, without exposing your traffic to the public internet**. AWS PrivateLink makes it easy to connect services across different accounts and VPCs to significantly simplify your network architecture. Interface **VPC endpoints**, powered by AWS PrivateLink, connect you to services hosted by AWS Partners and supported solutions available in AWS Marketplace. https://aws.amazon.com/privatelink/

This solution meets the requirements of creating digital copies for a large collection of historical written records, analyzing the documents, extracting the medical information, and storing the documents so that an application can run SQL queries on the data. Writing the document information to an Amazon S3 bucket can provide scalable and durable storage for the scanned files. Using Amazon Athena to query the data can provide serverless and interactive SQL analysis on data stored in S3. Creating an AWS Lambda function that runs when new documents are uploaded can provide event-driven and serverless processing of the scanned files. Using Amazon Textract to convert the documents to raw text can provide accurate optical character recognition (OCR) and extraction of structured data such as tables and forms from documents using artificial intelligence (AI). Using Amazon Comprehend Medical to detect and extract relevant medical information from the text can provide natural language processing (NLP) service that uses machine learning that has been pre-trained to understand and extract health data from medical text.

Option A is incorrect because writing the document information to an Amazon EC2 instance that runs a MySQL database can increase the infrastructure overhead and complexity, and it may not be able to handle large volumes of data. Option C is incorrect because creating an Auto Scaling group of Amazon EC2 instances to run a custom application that processes the scanned files and extracts the medical information can increase the infrastructure overhead and complexity, and it may not be able to leverage existing AI and NLP services such as Textract and Comprehend Medical. Option D is incorrect because using Amazon Rekognition to convert the documents to raw text can provide image and video analysis, but it does not support OCR or extraction of structured data from documents. Using Amazon Transcribe Medical to detect and extract relevant medical information from the text can provide speech-to-text transcription service for medical conversations, but it does not support text analysis or extraction of health data from medical text.

References:

• https://aws.amazon.com/s3/
• https://aws.amazon.com/athena/
• https://aws.amazon.com/lambda/
• https://aws.amazon.com/textract/
• https://aws.amazon.com/comprehend/medical/

Many applications, including those built on modern serverless architectures, can have a large number of open connections to the database server and may open and close database connections at a high rate, exhausting database memory and compute resources. Amazon RDS Proxy allows applications to pool and share connections established with the database, improving database efficiency and application scalability. https://aws.amazon.com/id/rds/proxy/

These options are operationally efficient because they use Amazon RDS read replicas to offload the reporting workload from the primary DB instance and avoid affecting any document modifications or the addition of new documents1. They also use Reserved Instances for the primary DB instance to reduce costs and On-Demand or Aurora Replicas for the read replicas to scale as needed. Option A is less efficient because it uses Amazon S3 Glacier Flexible Retrieval, which is a cold storage class that has higher retrieval costs and longer retrieval times than Amazon S3 Standard. It also uses EventBridge rules to invoke the job nightly, which does not meet the requirement of processing incoming data files as soon as possible. Option D is less efficient because it uses AWS Lambda to process the files, which has a maximum execution time of 15 minutes per invocation, which might not be enough for processing each file that needs 3-8 minutes. It also uses S3 event notifications to invoke the Lambda function when the files arrive, which could cause concurrency issues if there are thousands of small data files arriving periodically. Option E is less efficient because it uses Amazon DynamoDB, which is a NoSQL database service that does not support relational queries, which are needed for generating the reports. It also uses fixed write capacity, which could cause throttling or underutilization depending on the incoming data files.

- First 30 days- data access every morning ( predictable and frequently) – S3 standard - After 30 days, accessed 4 times a year – S3 infrequently access - Data preserved- S3 Gllacier Deep Archive

https://aws.amazon.com/blogs/security/how-to-use-aws-secrets-manager-rotate-credentials-amazon-rds-database-types-oracl e/

This answer is correct because it provides a resilient and durable replacement for the on-premises file share that is compatible with a serverless web application. Amazon S3 is a fully managed object storage service that can store any amount of data and serve it over the internet. It supports the following features:

• Resilience: Amazon S3 stores data across multiple Availability Zones within a Region, and offers 99.999999999% (11 9’s) of durability. It also supports cross-region replication, which enables automatic and asynchronous copying of objects across buckets in different AWS Regions.
• Durability: Amazon S3 encrypts data at rest using server-side encryption with either Amazon S3-managed keys (SSE-S3), AWS KMS keys (SSE-KMS), or customer-provided keys (SSE-C). It also supports encryption in transit using SSL/TLS. Amazon S3 also provides data protection features such as versioning, which keeps multiple versions of an object in the same bucket, and MFA Delete, which requires additional authentication for deleting an object version or changing the versioning state of a bucket.
• Performance: Amazon S3 delivers high performance and scalability for serving static and dynamic web content. It also supports features such as S3 Transfer Acceleration, which speeds up data transfers by routing requests to AWS edge locations, and S3 Select, which enables retrieving only a subset of data from an object by using simple SQL expressions.

The S3 Standard-Infrequent Access (S3 Standard-IA) storage class is suitable for storing images that are rarely accessed, but must be immediately available when needed. It offers the same high durability, throughput, and low latency as S3 Standard, but with a lower storage cost per GB and a higher per-request cost.

References:

• Amazon Simple Storage Service
• Storage classes - Amazon Simple Storage Service

This option is the most efficient because it uses AWS Key Management Service (AWS KMS), which is a service that makes it easy for you to create and manage cryptographic keys and control their use across a wide range of AWS services and with your applications running on AWS1. It also uses AWS KMS to encrypt the EBS volumes and Aurora database storage at rest, which provides data protection by encrypting your data with encryption keys that you manage23. It also uses AWS Certificate Manager (ACM), which is a service that lets you easily provision, manage, and deploy public and private Secure Sockets Layer/Transport Layer Security (SSL/TLS) certificates for use with AWS services and your internal connected resources. It also attaches an ACM certificate to the ALB to encrypt data in transit, which provides data protection by enabling SSL/TLS encryption for connections between clients and the load balancer. This solution meets the requirement of encrypting all data for the application at rest and in transit. Option A is less efficient because it uses AWS KMS certificates on the ALB to encrypt data in transit, which is not possible as AWS KMS does not provide certificates but only keys. It also uses AWS Certificate Manager (ACM) to encrypt the EBS volumes and Aurora database storage at rest, which is not possible as ACM does not provide encryption but only certificates. Option B is less efficient because it uses the AWS root account to log in to the AWS Management Console, which is not recommended as it has unrestricted access to all resources in your account. It also uploads the company’s encryption certificates, which is not necessary as ACM can provide certificates for free. It also selects the option to turn on encryption for all data at rest and in transit for the account, which is not possible as encryption settings are specific to each service and resource. Option D is less efficient because it uses BitLocker to encrypt all data at rest, which is a Windows feature that provides encryption for volumes on Windows servers. However, this does not provide encryption for Aurora database storage at rest, as Aurora runs on Linux servers. It also imports the company’s TLS certificate keys to AWS KMS, which is not necessary as ACM can provide certificates for free. It also attaches the KMS keys to the ALB to encrypt data in transit, which is not possible as ALB requires certificates and not keys.

This answer is correct because it allows the web tier to access the database tier by using security groups as a source, which is a recommended best practice for VPC connectivity. Security groups are stateful and can reference other security groups in the same VPC, which simplifies the configuration and maintenance of the firewall rules. By adding an inbound rule to the database tier’s security group, the web tier’s EC2 instances can connect to the RDS instance on port 3306, regardless of their IP addresses or subnets.

References:

• Security groups - Amazon Virtual Private Cloud
• Best practices and reference architectures for VPC design

A solutions architect should recommend option A, which is to configure AWS WAF rules and associate them with the ALB. This will allow the company to apply traffic filtering at the application layer, which is necessary for protecting the ALB against common application-level attacks such as cross-site scripting or SQL injection. AWS WAF is a managed service that makes it easy to protect web applications from common web exploits that could affect application availability, compromise security, or consume excessive resources. The company can easily manage and update the rules to ensure the security of its application.

Amazon DocumentDB (with MongoDB compatibility) is a fast, reliable, and fully managed database service. Amazon DocumentDB makes it easy to set up, operate, and scale MongoDB-compatible databases in the cloud. With Amazon DocumentDB, you can run the same application code and use the same drivers and tools that you use with MongoDB.

https://docs.aws.amazon.com/documentdb/latest/developerguide/what-is.html

Using Amazon SQS will help minimize the number of connections to the database, as the API will write data to a queue instead of directly to the database. Additionally, using an AWS Lambda function that Amazon SQS invokes to write data from the queue to the database will help ensure that data is not lost during periods of heavy traffic, as the queue will serve as a buffer between the API and the database.

This solution meets the requirements most cost-effectively because it enables the company to migrate its on-premises NFS data store to AWS without changing the existing applications or workflows. AWS Storage Gateway is a hybrid cloud storage service that provides seamless and secure integration between on-premises and AWS storage. Amazon S3 File Gateway is a type of AWS Storage Gateway that provides a file interface to Amazon S3, with local caching for low-latency access. By setting up an Amazon S3 File Gateway, the company can store and retrieve files as objects in Amazon S3 using standard file protocols such as NFS. The company can also use an Amazon S3 Lifecycle policy to automatically transition the data to the appropriate storage class based on the frequency of access and the cost of storage. For example, the company can use S3 Standard for frequently accessed data, S3 Standard-Infrequent Access (S3 Standard-IA) or S3 One Zone-Infrequent Access (S3 One Zone-IA) for less frequently accessed data, and S3 Glacier or S3 Glacier Deep Archive for long-term archival data.

Option A is not a valid solution because AWS Storage Gateway Volume Gateway is a type of AWS Storage Gateway that provides a block interface to Amazon S3, with local caching for low-latency access. Volume Gateway is not suitable for migrating an NFS data store, as it requires attaching the volumes to EC2 instances or on-premises servers using the iSCSI protocol. Option C is not a valid solution because Amazon Elastic File System (Amazon EFS) is a fully managed elastic NFS file system that is designed for workloads that require high availability, scalability, and performance. Amazon EFS Standard-Infrequent Access (Standard-IA) is a storage class within Amazon EFS that is optimized for infrequently accessed files, with a lower price per GB and a higher price per access. Using Amazon EFS Standard-IA for migrating an NFS data store would not be cost-effective, as it would incur higher access charges and require additional configuration to enable lifecycle management. Option D is not a valid solution because Amazon EFS One Zone-Infrequent Access (One Zone-IA) is a storage class within Amazon EFS that is optimized for infrequently accessed files that do not require the availability and durability of Amazon EFS Standard or Standard-IA. Amazon EFS One Zone-IA stores data in a single Availability Zone, which reduces the cost by 47% compared to Amazon EFS Standard-IA, but also increases the risk of data loss in the event of an Availability Zone failure. Using Amazon EFS One Zone-IA for migrating an NFS data store would not be cost-effective, as it would incur higher access charges and require additional configuration to enable lifecycle management. It would also compromise the availability and durability of the data.

References:

• AWS Storage Gateway - Amazon Web Services
• Amazon S3 File Gateway - AWS Storage Gateway
• Object Lifecycle Management - Amazon Simple Storage Service
• [AWS Storage Gateway Volume Gateway - AWS Storage Gateway]
• [Amazon Elastic File System - Amazon Web Services]
• [Using EFS storage classes - Amazon Elastic File System]

https://docs.aws.amazon.com/AmazonS3/latest/userguide/WebsiteHosting.html

The Kubernetes Cluster Autoscaler is a component that automatically adjusts the number of nodes in your cluster when pods fail or are rescheduled onto other nodes. It uses Auto Scaling groups to scale up or down the nodes according to the demand and capacity of your cluster1.

By using the Kubernetes Cluster Autoscaler in your Amazon EKS cluster, you can achieve the following benefits:

• You can improve the performance and availability of your container applications by ensuring that there are enough nodes to run your pods and that there are no idle nodes wasting resources.
• You can reduce the administrative overhead of managing your cluster size manually or using custom scripts. The Cluster Autoscaler handles the scaling decisions and actions for you based on the metrics and events from your cluster.
• You can leverage the integration of Amazon EKS and AWS Auto Scaling to optimize the cost and efficiency of your cluster. You can use features such as launch templates, mixed instances policies, and spot instances to customize your node configuration and save up to 90% on compute costs2

https://docs.aws.amazon.com/vpc/latest/privatelink/vpc-endpoints.html

To allow the MySQL database in the private subnets to access the internet without exposing it to the public, a NAT gateway is a suitable solution. A NAT gateway enables instances in a private subnet to connect to the internet or other AWS services, but prevents the internet from initiating a connection with those instances. A NAT gateway resides in the public subnets and can handle high throughput of traffic with low latency. A NAT gateway is also a managed service that does not require any operational overhead.

References:

• NAT Gateways
• NAT Gateway Pricing

AWS Global Accelerator is a networking service that improves the performance and availability of applications for global users. It uses the AWS global network to route user traffic to the optimal endpoint based on performance and health. It also provides static IP addresses that act as a fixed entry point to the applications and support both TCP and UDP protocols1. By using AWS Global Accelerator, the solution can ensure the lowest possible latency for all users.

A. Use AWS Global Accelerator to create an accelerator. Create an Application Load Balancer (ALB) behind an accelerator endpoint that uses Global Accelerator integration and listening on the TCP and UDP ports. Update the Auto Scaling group to register instances on the ALB. This solution will not work, as ALB does not support UDP protocol2.

C. Create an Amazon CloudFront content delivery network (CDN) endpoint. Create a Network Load Balancer (NLB) behind the endpoint and listening on the TCP and UDP ports. Update the Auto Scaling group to register instances on the NLB. Update CloudFront to use the NLB as the origin. This solution will not work, as CloudFront does not support UDP protocol3.

D. Create an Amazon Cloudfront content delivery network (CDN) endpoint. Create an Application Load Balancer (ALB) behind the endpoint and listening on the TCP and UDP ports. Update the Auto Scaling group to register instances on the ALB. Update CloudFront to use the ALB as the origin. This solution will not work, as CloudFront and ALB do not support UDP protocol23.

Reference URL: https://aws.amazon.com/global-accelerator/

Option C provides moving the website images onto an Amazon EFS file system that is mounted on every EC2 instance. Amazon EFS provides a scalable and fully managed file storage solution that can be accessed concurrently from multiple EC2 instances. This ensures that the website images can be accessed efficiently and consistently by all instances, improving performance In Option E The Auto Scaling group maintains a minimum of two instances, ensuring resilience by automatically replacing any unhealthy instances. Additionally, configuring an Amazon CloudFront distribution for the website further improves performance by caching content at edge locations closer to the end-users, reducing latency and improving content delivery. Hence combining these actions, the website's performance is improved through efficient image storage and content delivery

To back up EC2 instances and RDS DB instances in a separate Region with the least operational overhead, AWS Backup is a simple and cost-effective solution. AWS Backup can copy EC2 backups and RDS backups to another Region automatically and securely. AWS Backup also supports backup policies, retention rules, and monitoring features.

References:

• What Is AWS Backup?
• Cross-Region Backup

Versioning is a feature of Amazon S3 that allows users to keep multiple versions of the same object in a bucket. It can help prevent accidental deletion of the documents and ensure that all versions of the documents are available1. MFA Delete is a feature of Amazon S3 that adds an extra layer of security by requiring two forms of authentication to delete a version or change the versioning state of a bucket. It can help prevent unauthorized or accidental deletion of the documents2. By enabling both versioning and MFA Delete on the bucket, the solution can meet the requirements.

A. Enable a read-only bucket ACL. This solution will not meet the requirement of allowing users to download, modify, and upload documents, as a read-only bucket ACL will prevent write access to the bucket3.

C. Attach an IAM policy to the bucket. This solution will not meet the requirement of preventing accidental deletion of the documents and ensuring that all versions of the documents are available, as an IAM policy is used to grant or deny permissions to users or roles, not to enable versioning or MFA Delete4.

E. Encrypt the bucket using AWS KMS. This solution will not meet the requirement of preventing accidental deletion of the documents and ensuring that all versions of the documents are available, as encrypting the bucket using AWS KMS is a method of protecting data at rest, not enabling versioning or MFA Delete.

Reference URL: https://docs.aws.amazon.com/AmazonS3/latest/userguide/Versioning.html

A read replica is a copy of the primary database that supports read-only queries. By creating a read replica, you can offload the read workload from the primary database and improve its performance. The script can query the read replica without affecting the critical application that uses the primary database. This solution also has the least operational overhead, as you do not need to modify the script, export the data manually, or manage a cache cluster. References:

• Working with PostgreSQL, MySQL, and MariaDB Read Replicas
• Amazon RDS Performance Insights

https://aws.amazon.com/shield/faqs/

https://docs.aws.amazon.com/AmazonS3/latest/userguide/privatelink-interface-endpoints.html#types-of-vpc-endpoints-for-s3

https://docs.aws.amazon.com/vpc/latest/userguide/vpc-endpoints-s3.html

Amazon S3 is a highly scalable and durable object storage service that can store and retrieve any amount of data from anywhere on the web1. Users can configure the application to upload images directly from each user’s browser to Amazon S3 through the use of a presigned URL. A presigned URL is a URL that gives access to an object in an S3 bucket for a limited time and with a specific action, such as uploading an object2. Users can generate a presigned URL programmatically using the AWS SDKs or AWS CLI. By using a presigned URL, users can reduce coupling within the application and improve website performance, as they do not need to send the images to the web server first.

AWS Lambda is a serverless compute service that runs code in response to events and automatically manages the underlying compute resources3. Users can configure S3 Event Notifications to invoke an AWS Lambda function when an image is uploaded. S3 Event Notifications is a feature that allows users to receive notifications when certain events happen in an S3 bucket, such as object creation or deletion. Users can configure S3 Event Notifications to invoke a Lambda function that resizes the image and stores it back in the same or a different S3 bucket. This way, users can offload the image resizing task from the web server to Lambda.

Use a group email address for the management account's root user https://docs.aws.amazon.com/organizations/latest/userguide/orgs_best-practices_mgmt-acct.html#best-practices_mgmt-acct_email-address

https://aws.amazon.com/blogs/networking-and-content-delivery/deliver-your-apps-dynamic-content-using-amazon-cloudfront-getting-started-template/

https://aws.amazon.com/about-aws/whats-new/2017/10/aws-waf-now-supports-geographic-match/

1. lowest possible latency + node to node ==> cluster placement(must be within one AZ), so C, D out

2. For EBS Multi-Attach, up to 16 instances can be attached to a single volume==>we have 16 linux instance==>more close to A

3. "need a shared block device volume"==>EBS Multi-attach is Block Storage whereas EFS is File Storage==> B out

4. EFS automatically replicates data within and across 3 AZ==>we use cluster placement so all EC2 are within one AZ.

5. EBS Multi-attach volumes can be used for clients within a single AZ.

https://repost.aws/questions/QUK2RANw1QTKCwpDUwCCI72A/efs-vs-ebs-mult-attach

https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/InstanceStorage.html

• Amazon S3 for large-scale, durable, and inexpensive storage of the video content. S3 storage costs are significantly lower than EFS. • Amazon EBS only temporarily during processing. By mounting an EBS volume only when a video needs to be processed, and unmounting it after, the time the content spends on the higher-cost EBS storage is minimized. • The EBS volume can be sized to match the workload needs for active processing, keeping costs lower. The volume does not need to store the entire video library long-term.

S3 Intelligent-Tiering is a cost-optimized storage class that automatically moves data to the most cost-effective access tier based on changing access patterns. Although it offers cost savings, it also introduces additional latency and retrieval time into the data retrieval process, which may not meet the requirement of "immediately available" data. On the other hand, S3 Standard-Infrequent Access (S3 Standard-IA) provides low cost storage with low latency and high throughput performance. It is designed for infrequently accessed data that can be recreated if lost, and can be retrieved in a timely manner if required. It is a cost-effective solution that meets the requirement of immediately available data and remains accessible for up to 3 months.

Use AWS Batch on Amazon EC2. AWS Batch is a fully managed batch processing service that can be used to easily run batch jobs on Amazon EC2 instances. It can scale the number of instances to match the workload, allowing the batch job to be completed in the desired time frame with minimal operational overhead.

Using AWS Lambda with Amazon API Gateway - AWS Lambda

https://docs.aws.amazon.com/lambda/latest/dg/services-apigateway.html

AWS Lambda FAQs

https://aws.amazon.com/lambda/faqs/

Amazon EC2 Auto Scaling offers the ability to add lifecycle hooks to your Auto Scaling groups. These hooks let you create solutions that are aware of events in the Auto Scaling instance lifecycle, and then perform a custom action on instances when the corresponding lifecycle event occurs. (https://docs.aws.amazon.com/autoscaling /ec2/userguide/lifecycle-hooks.html)

This option is the most efficient because it uses Cost Explorer, which is a tool that allows you to visualize, understand, and manage your AWS costs and usage over time1. You can create a report in Cost Explorer that lists AWS billed items by user, using the user name tag as a filter2. You can then download the report as a CSV file and use it for budget planning. Option A is less efficient because it uses Amazon Athena, which is a serverless interactive query service that allows you to analyze data in Amazon S3 using standard SQL3. You would need to set up an Athena table that points to your AWS Cost and Usage Report data in S3, and then run a query to generate the report. This would incur additional costs and complexity. Option C is less efficient because it uses the billing dashboard, which provides a high-level summary of your AWS costs and usage. You can access the bill details from the billing dashboard and download them via bill, but this would not list the billed items by user. You would need to use tags to group your costs by user name, which would require additional steps. Option D is less efficient because it uses AWS Budgets, which is a tool that allows you to plan your service usage, service costs, and instance reservations. You can modify a cost budget in AWS Budgets to alert with Amazon Simple Email Service (Amazon SES), but this would not generate a report of AWS billed items by user. This would only notify you when your actual or forecasted costs exceed or are expected to exceed your budgeted amount.

https://aws.amazon.com/fsx/lustre/

Amazon FSx for Lustre is a fully managed service that provides cost-effective, high-performance, scalable storage for compute workloads. Many workloads such as machine learning, high performance computing (HPC), video rendering, and financial simulations depend on compute instances accessing the same set of data through high-performance shared storage.

RDS supported Storage > https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/CHAP_Storage.html GP2 max IOPS > https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/general-purpose.html#gp2-performance

Amazon RDS provides three storage types: General Purpose SSD (also known as gp2 and gp3), Provisioned IOPS SSD (also known as io1), and magnetic (also known as standard). They differ in performance characteristics and price, which means that you can tailor your storage performance and cost to the needs of your database workload. You can create MySQL, MariaDB, Oracle, and PostgreSQL RDS DB instances with up to 64 tebibytes (TiB) of storage. You can create SQL Server RDS DB instances with up to 16 TiB of storage. For this amount of storage, use the Provisioned IOPS SSD and General Purpose SSD storage types. https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/CHAP_Storage.html

Create an Amazon S3 bucket to store the raw data Create an Amazon FSx for Lustre file system that uses persistent SSD storage Select the option to import data from and export data to Amazon S3 Mount the file system on the EC2 instances. Amazon FSx for Lustre uses SSD storage for sub-millisecond latencies and up to 6 GBps throughput, and can import data from and export data to Amazon S3. Additionally, the option to select persistent SSD storage will ensure that the data is stored on the disk and not lost if the file system is stopped.

Elastic BeanStalk is expensive, and DocumentDB has a 400KB max to upload files. So Lambda and S3 should be the one.

https://aws.amazon.com/blogs/security/how-to-replicate-secrets-aws-secrets-m anager-multiple-regions/

as the policy prevents anyone from doing any EC2 action on any region except us-east-1 and allows only users with source ip 10.100.100.0/24 to terminate instances. So user with source ip 10.100.100.254 can terminate instances in us-east-1 region.

"In some cases, this connection alone is not enough. It is always better to guarantee a fallback connection as the backup of DX. There are several options, but implementing it with an AWS Site-To-Site VPN is a real cost-effective solution that can be exploited to reduce costs or, in the meantime, wait for the setup of a second DX."

https://www.proud2becloud.com/hybrid-cloud-networking-backup-aws-direct-connect-network-connection-with-aws-site-to-site-vpn/

"The Object Lock legal hold operation enables you to place a legal hold on an object version. Like setting a retention period, a legal hold prevents an object version from being overwritten or deleted. However, a legal hold doesn't have an associated retention period and remains in effect until removed." https://docs.aws.amazon.com/AmazonS3/latest/userguide/batch-ops-legal-hold.html

"Security groups create an outbound rule for every inbound rule." Not completely right. Statefull does NOT mean that if you create an inbound (or outbound) rule, it will create an outbound (or inbound) rule. What it does mean is: suppose you create an inbound rule on port 443 for the X ip. When a request enters on port 443 from X ip, it will allow traffic out for that request in the port 443. However, if you look at the outbound rules, there will not be any outbound rule on port 443 unless explicitly create it. In ACLs, which are stateless, you would have to create an inbound rule to allow incoming requests and an outbound rule to allow your application responds to those incoming requests.

https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#SecurityGroupRules

https://aws.amazon.com/blogs/aws/amazon-aurora-fast-database-cloning/

https://aws.amazon.com/fsx/lustre/

Amazon FSx has native support for Windows file system features and for the industry-standard Server Message Block (SMB) protocol to access file storage over a network. https://docs.aws.amazon.com/fsx/latest/WindowsGuide/what-is.html

https://aws.amazon.com/cn/blogs/security/how-to-connect-to-aws-secre ts-manager-service-within-a-virtual-private-cloud/

https://aws.amazon.com/blogs/security/rotate-amazon-rds-database-creden tials-automatically-with-aws-secrets-manager/

The destination of your Kinesis Data Firehose delivery stream. Kinesis Data Firehose can send data records to various destinations, including Amazon Simple Storage Service (Amazon S3), Amazon Redshift, Amazon OpenSearch Service, and any HTTP endpoint that is owned by you or any of your third-party service providers. The following are the supported destinations:

* Amazon OpenSearch Service

* Amazon S3

* Datadog

* Dynatrace

* Honeycomb

* HTTP Endpoint

* Logic Monitor

* MongoDB Cloud

* New Relic

* Splunk

* Sumo Logic

https://docs.aws.amazon.com/firehose/latest/dev/create-name.html

https://aws.amazon.com/kinesis/data-streams/

Amazon Kinesis Data Streams (KDS) is a massively scalable and durable real-time data streaming service. KDS can continuously capture gigabytes of data per second from hundreds of thousands of sources such as website clickstreams, database event streams, financial transactions, social media feeds, IT logs, and location-tracking events.

https://aws.amazon.com/premiumsupport/knowledge-center/acm-certificate-expiration/

https://docs.aws.amazon.com/IAM/latest/UserGuide/reference_aws-services-that-work-with-iam.html

1. database shows no signs of being overloaded. CPU, memory, and disk access metrics are all low==>A and C out. We cannot only add nodes instance or add read replica, because database workload is totally fine, very low. 2. "least operational overhead"==>B out, because b need to configure lambda. 3. ROS proxy: Shares infrequently used connections; High availability with failover; Drives increased efficiency==>proxy can leverage failover to redirect traffic from timeout rds instance to healthy rds instance. So D is right.

AWS Network Firewall supports both inspection and filtering as required

The solution that meets the requirements with the least operational overhead is to migrate the web tier to Amazon EC2 instances in public subnets, migrate the application tier to EC2 instances in private subnets, and migrate the database tier to Amazon RDS for MySQL in private subnets. This solution allows the company to migrate its three-tier application to AWS by making minimal changes to the architecture, as it preserves the same web, application, and database tiers and uses the same MySQL database engine. The solution also provides a database solution that can restore data to a specific point in time, as Amazon RDS for MySQL supports automated backups and point-in-time recovery. This solution also reduces the operational overhead by using managed services such as Amazon EC2 and Amazon RDS, which handle tasks such as provisioning, patching, scaling, and monitoring.

The other solutions do not meet the requirements as well as the first one because they either involve more changes to the architecture, do not provide point-in-time recovery, or do not follow best practices for security and availability. Migrating the database tier to Amazon Aurora MySQL would require changing the database engine and potentially modifying the application code to ensure compatibility. Migrating the web tier and the application tier to public subnets would expose them to more security risks and reduce their availability in case of a subnet failure. Migrating the database tier to public subnets would also compromise its security and performance. References:

• Migrate Your Application Database to Amazon RDS
• Amazon RDS for MySQL
• Amazon Aurora MySQL
• Amazon VPC

https://docs.aws.amazon.com/IAM/latest/UserGuide/access_policies.html

https://docs.aws.amazon.com/accounts/latest/reference/best-practices-root-user.html * Enable AWS multi-factor authentication (MFA) on your AWS account root user. For more information, see Using multi-factor authentication (MFA) in AWS in the IAM User Guide. * Never share your AWS account root user password or access keys with anyone. * Use a strong password to help protect access to the AWS Management Console. For information about managing your AWS account root user password, see Changing the password for the root user.

https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/rds-custom.html and https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/working-with-custom-oracle.html

AWS Backup is a fully managed service that enables users to centralize and automate the backup of data across AWS services. It can create and manage backup plans that specify the frequency and retention period of backups. It can also assign backup resources to backup vaults, which are containers that store backup data1. By using AWS Backup, the solution can ensure that the RDS backups are consistent, restorable, and retained for a minimum period of 2 years.

B. Configure a backup window for the RDS DB instances for daily snapshots. Assign a snapshot retention policy of 2 years to each RDS DB instance. Use Amazon Data Lifecycle Manager (Amazon DLM) to schedule snapshot deletions. This solution will not meet the requirement of ensuring that the backups are consistent and restorable, as Amazon DLM is not compatible with RDS snapshots and cannot be used to schedule snapshot deletions2.

C. Configure database transaction logs to be automatically backed up to Amazon CloudWatch Logs with an expiration period of 2 years. This solution will not meet the requirement of ensuring that the backups are consistent and restorable, as database transaction logs are not sufficient to restore a database to a point in time. They only capture the changes made to the database, not the full state of the database3.

D. Configure an AWS Database Migration Service (AWS DMS) replication task. Deploy a replication instance, and configure a change data capture (CDC) task to stream database changes to Amazon S3 as the target. Configure S3 Lifecycle policies to delete the snapshots after 2 years. This solution will not meet the requirement of ensuring that the backups are consistent and restorable, as AWS DMS is a service that helps users migrate databases to AWS, not back up databases. It also requires additional resources and configuration, such as replication instances and CDC tasks.

Reference URL: https://docs.aws.amazon.com/aws-backup/latest/devguide/whatisbackup.html

The most suitable solution for the company’s compliance policy is to enable the restricted-ssh AWS Config managed rule and generate an Amazon Simple Notification Service (Amazon SNS) notification when a noncompliant rule is created. This solution has the least operational overhead because it uses a predefined rule that is already available in AWS Config, which is a service that enables users to assess, audit, and evaluate the configurations of their AWS resources. The restricted-ssh rule checks whether security groups that are in use have inbound rules that allow SSH from 0.0.0.0/0 addresses, and reports them as noncompliant1. Users can configure the rule to send notifications to an Amazon SNS topic when a noncompliant change occurs, and subscribe to the topic to receive alerts via email, SMS, or other methods2.

The other options are not correct because they either have more operational overhead or do not meet the requirements. Writing an AWS Lambda script that monitors security groups for SSH being open to 0.0.0.0/0 addresses and creates a notification every time it finds one is not correct because it requires custom code development and maintenance, which adds complexity and cost to the solution. Creating an IAM role with permissions to globally open security groups and network ACLs, and creating an Amazon SNS topic to generate a notification every time the role is assumed by a user is not correct because it does not prevent or detect the creation of noncompliant rules by other users or roles, and it does not address the existing rules that may violate the policy. Configuring a service control policy (SCP) that prevents non-administrative users from creating or editing security groups, and creating a notification in the ticketing system when a user requests a rule that needs administrator permissions is not correct because it does not provide an automated solution for the policy enforcement and notification, and it may limit the flexibility and productivity of the users.

References:

• restricted-ssh - AWS Config
• Getting Notifications When Your Resources Change - AWS Config

company need to setup a cheaper connection (200 M) but B is incorrect because you can only order port speeds of 1, 10, or 100 Gbps for more flexibility you can go with hosted connection, You can order port speeds between 50 Mbps and 10 Gbps. https://docs.aws.amazon.com/whitepapers/latest/aws-vpc-connectivity-options/aws-direct-connect.html

it allows the company to monitor costs and notify responsible stakeholders in the event of unusual spending. By creating an AWS Cost Anomaly Detection monitor in the AWS Billing and Cost Management console, the company can use a machine learning service that automatically detects and alerts on anomalous spend. By configuring alert thresholds, notification preferences, and root cause analysis, the company can prevent unusual spending and identify its source. References:

• AWS Cost Anomaly Detection
• Creating a Cost Anomaly Monitor

• Creating an Amazon Simple Queue Service (SQS) queue and configuring the S3 bucket to send a notification to the SQS queue when an image is uploaded to the S3 bucket will ensure that the Lambda function is triggered in a stateless and durable manner.
• Configuring the Lambda function to use the SQS queue as the invocation source, and deleting the message in the queue after it is successfully processed will ensure that the Lambda function processes the image in a stateless and durable manner.

Amazon SQS is a fully managed message queuing service that enables you to decouple and scale microservices, distributed systems, and serverless applications. SQS eliminates the complexity and overhead associated with managing and operating-message oriented middleware, and empowers developers to focus on differentiating work. When new images are uploaded to the S3 bucket, SQS will trigger the Lambda function to process the image and compress it. Once the image is processed, the SQS message is deleted, ensuring that the Lambda function is stateless and durable.

AURORA is 5x performance improvement over MySQL on RDS and handles more read requests than write,; maintaining high availability = Multi-AZ deployment

EC2 Spot Instances allow users to bid on spare Amazon EC2 computing capacity and can be a cost-effective solution for stateless, interruptible workloads that can be started and stopped at any time. Since the batch processing job is stateless, can be started and stopped at any time, and typically takes upwards of 60 minutes to complete, EC2 Spot Instances would be a good fit for this workload.

This solution meets the requirements because it allows the company to use both predictive scaling and dynamic scaling to optimize the capacity of its Auto Scaling group. Predictive scaling uses machine learning to analyze historical data and forecast future traffic patterns. It then adjusts the desired capacity of the group in advance of the predicted changes. Dynamic scaling uses target tracking to maintain a specified metric (such as CPU utilization) at a target value. It scales the group in or out as needed to keep the metric close to the target. By using both scaling methods, the company can benefit from faster, simpler, and more accurate scaling that responds to both forecasted and live changes in utilization.

References:

• Predictive scaling for Amazon EC2 Auto Scaling
• [Target tracking scaling policies for Amazon EC2 Auto Scaling]

Amazon S3 is cheapest and can be accessed from anywhere.

https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/private-content-restricting-access-to-s3.html

https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/distribution-web- awswaf.html

https://aws.amazon.com/about-aws/whats-new/2018/03/introducing-amazon-vpc-nat-gateway-in-the-aws-govcloud-us-region/#:~:text=NAT%20Gateway%20is%20a%20highly,instances%20in %20a%20private%20subnet.

https://docs.aws.amazon.com/vpc/latest/userguide/vpc-nat-comparison.html

https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/USER_MySQL.Replication.ReadReplicas.html

The correct answer is Option D. Deploy an S3 VPC gateway endpoint into the VPC and attach an endpoint policy that allows access to the S3 buckets. By deploying an S3 VPC gateway endpoint, the application can access the S3 buckets over a private network connection within the VPC, eliminating the need for data transfer over the internet. This can help reduce data transfer fees as well as improve the performance of the application. The endpoint policy can be used to specify which S3 buckets the application has access to.

To launch a one-deal-a-day website on AWS with millisecond latency during peak hours and with the least operational overhead, the best option is to use an Amazon S3 bucket to host the website's static content, deploy an Amazon CloudFront distribution, set the S3 bucket as the origin, use Amazon API Gateway and AWS Lambda functions for the backend APIs, and store the data in Amazon DynamoDB. This option requires minimal operational overhead and can handle millions of requests each hour with millisecond latency during peak hours. Therefore, option D is the correct answer.

To ensure that orders are processed in the order that they are received, the best solution is to use an Amazon SQS FIFO (First-In-First-Out) queue. This type of queue maintains the exact order in which messages are sent and received. In this case, the application can send information about new orders to an Amazon API Gateway REST API, which can then use an API Gateway integration to send a message to an Amazon SQS FIFO queue for processing. The queue can then be configured to invoke an AWS Lambda function to perform the necessary processing on each order. This ensures that orders are processed in the exact order in which they are received.

https://docs.aws.amazon.com/systems-manager/latest/userguide/about-windows-app-patching.html

In Static Websites, Web pages are returned by the server which are prebuilt.

They use simple languages such as HTML, CSS, or JavaScript.

There is no processing of content on the server (according to the user) in Static Websites. Web pages are returned by the server with no change therefore, static Websites are fast.

There is no interaction with databases.

Also, they are less costly as the host does not need to support server-side processing with different languages.

============

In Dynamic Websites, Web pages are returned by the server which are processed during runtime means they are not prebuilt web pages but they are built during runtime according to the user’s demand.

These use server-side scripting languages such as PHP, Node.js, ASP.NET and many more supported by the server.

So, they are slower than static websites but updates and interaction with databases are possible.

The visibility timeout begins when Amazon SQS returns a message. During this time, the consumer processes and deletes the message. However, if the consumer fails before deleting the message and your system doesn't call the DeleteMessage action for that message before the visibility timeout expires, the message becomes visible to other consumers and the message is received again. If a message must be received only once, your consumer should delete it within the duration of the visibility timeout. https://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSDeveloperGuide/sqs-visibility-timeout.html

Keyword: SQS queue writes to an Amazon RDS From this, Option D best suite & other Options ruled out [Option A - You can't intruduce one more Queue in the existing one; Option B - only Permission & Option C - Only Retrieves Messages] FIF O queues are designed to never introduce duplicate messages. However, your message producer might introduce duplicates in certain scenarios: for example, if the producer sends a message, does not receive a response, and then resends the same message. Amazon SQS APIs provide deduplication functionality that prevents your message producer from sending duplicates. Any duplicates introduced by the message producer are removed within a 5-minute deduplication interval. For standard queues, you might occasionally receive a duplicate copy of a message (at-least- once delivery). If you use a standard queue, you must design your applications to be idempotent (that is, they must not be affected adversely when processing the same message more than once).

To provide the product manager access to the Amazon CloudWatch dashboard while following the principle of least privilege, a solution architect should create an IAM user specifically for the product manager and attach the CloudWatch Read Only Access managed policy to the user. This policy allows the user to view the dashboard without being able to make any changes to it. The solution architect should then share the new login credential with the product manager and provide them with the browser URL of the correct dashboard.

This approach ensures that the order creation and payment processing steps are separate and atomic. By sending the order information to an SQS FIFO queue, the payment service can process the order one at a time and in the order they were received. If the payment service is unable to process an order, it can be retried later, preventing the creation of multiple orders. The deletion of the message from the queue after it is processed will prevent the same message from being processed multiple times.

https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resour ce-ecs-taskdefinition.html

The solution that will reduce the launch time of the application during the next testing phase is to launch the EC2 On-Demand Instances with hibernation turned on and configure EC2 Auto Scaling warm pools. This solution allows the application to resume from a hibernated state instead of starting from scratch, which can save time and resources. Hibernation preserves the memory (RAM) state of the EC2 instances to the root EBS volume and then stops the instances. When the instances are resumed, they restore their memory state from the EBS volume and become productive quickly. EC2 Auto Scaling warm pools can be used to maintain a pool of pre-initialized instances that are ready to scale out when needed. Warm pools can also support hibernated instances, which can further reduce the launch time and cost of scaling out.

The other solutions are not as effective as the first one because they either do not reduce the launch time, do not guarantee availability, or do not use On-Demand Instances as required. Launching two or more EC2 On-Demand Instances with auto scaling features does not reduce the launch time of the application, as each instance still has to go through the initialization process. Launching EC2 Spot Instances does not guarantee availability, as Spot Instances can be interrupted by AWS at any time when there is a higher demand for capacity. Launching EC2 On-Demand Instances with Capacity Reservations does not reduce the launch time of the application, as it only ensures that there is enough capacity available for the instances, but does not pre-initialize them.

References:

• Hibernating your instance - Amazon Elastic Compute Cloud
• Warm pools for Amazon EC2 Auto Scaling - Amazon EC2 Auto Scaling

To use an SSL/TLS certificate that is issued by an external CA, the certificate must be imported to AWS Certificate Manager (ACM). ACM can send a notification when the certificate is nearing expiration, but it cannot automatically rotate the certificate. Therefore, the certificate must be rotated manually by importing a new certificate and applying it to the ALB.

References:

• Importing Certificates into AWS Certificate Manager
• Renewing and Rotating Imported Certificates
• Using an ACM Certificate with an Application Load Balancer

it allows the company to store and deliver images to users in a highly available and cost-effective way. By storing images in Amazon S3 Standard, the company can use a durable, scalable, and secure object storage service that offers high availability and performance. By using S3 Standard to directly deliver images by using a static website, the company can avoid running web servers and reduce operational overhead. S3 Standard also offers low storage pricing and free data transfer within AWS Regions. References:

• Amazon S3 Storage Classes
• Hosting a Static Website on Amazon S3

it allows the compliance team to manage the KMS keys used for server-side encryption, thereby providing the necessary control over the encryption keys. Additionally, the use of the "aws:SecureTransport" condition on the bucket policy ensures that all connections to the S3 bucket are encrypted in transit.

This answer is correct because it reflects the effect of the IAM policy on the group members. The policy has two statements: one with an Allow effect and one with a Deny effect. The Allow statement grants permission to perform any EC2 action on any resource within the us-east-1 Region. The Deny statement overrides the Allow statement and denies permission to perform the ec2:StopInstances and ec2:TerminateInstances actions on any resource within the us-east-1 Region, unless the group member is logged in with MFA. Therefore, the group members can perform any EC2 action except stopping or terminating instances in the us-east-1 Region, unless they use MFA.

Amazon S3 File Gateway is a service that provides a file-based interface to Amazon S3, which appears as a network file share. It enables you to store and retrieve Amazon S3 objects through standard file storage protocols such as SMB. S3 File Gateway can also cache frequently accessed data locally for low-latency access. S3 Lifecycle policy is a feature that allows you to define rules that automate the management of your objects throughout their lifecycle. You can use S3 Lifecycle policy to transition objects to different storage classes based on their age and access patterns. S3 Glacier Deep Archive is a storage class that offers the lowest cost for long-term data archiving, with a retrieval time of 12 hours or 48 hours. This solution will meet the requirements, as it allows the company to store large files in S3 with SMB file access, and to move the files to S3 Glacier Deep Archive after 7 days for cost savings and compliance.

References:

• 1 provides an overview of Amazon S3 File Gateway and its benefits.
• 2 explains how to use S3 Lifecycle policy to manage object storage lifecycle.
• 3 describes the features and use cases of S3 Glacier Deep Archive storage class.

https://aws.amazon.com/rds/features/multi-az/ To convert an existing Single-AZ DB Instance to a Multi-AZ deployment, use the "Modify" option corresponding to your DB Instance in the AWS Management Console.

A cross-account IAM role is a way to grant users from one AWS account access to resources in another AWS account. The cross-account IAM role can have a read-only access policy attached to it, which allows the users to download objects from the S3 buckets without modifying or deleting them. The cross-account IAM role also reduces the operational overhead of managing multiple IAM users and policies in each account. The cross-account IAM role meets all the requirements of the question, while the other options do not. References:

• https://docs.aws.amazon.com/AmazonS3/latest/userguide/example-walkthroughs-managing-access-example2.html
• https://aws.amazon.com/blogs/storage/setting-up-cross-account-amazon-s3-access-with-s3-access-points/
• https://docs.aws.amazon.com/IAM/latest/UserGuide/id_roles_create_for-user_externalid.html

it allows the company to create read replicas of its RDS database and reduce the load on the database tier. By creating read replicas, the company can offload read traffic from the primary database instance to one or more replicas. By configuring the reports to use the new read replicas, the company can improve performance and availability of its database tier. References:

• Working with Read Replicas
• Read Replicas for Amazon RDS for SQL Server

This option is the most cost-effective solution because it leverages the Spot Instances, which are unused EC2 instances that are available at up to 90% discount compared to On-Demand prices. Spot Instances can be interrupted by AWS when the demand for On-Demand instances increases, but since the batch jobs are fault-tolerant and can be reprocessed by another instance, this is not a major issue. By using a launch template, the company can specify the configuration of the Spot Instances, such as the instance type, the operating system, and the user data. By using an Auto Scaling group, the company can automatically scale the number of Spot Instances based on the CPU usage, which reflects the load of the batch jobs. This way, the company can optimize the performance and the cost of the EC2 instances for the nightly batch jobs.

A. Purchase a 1-year Savings Plan for Amazon EC2 that covers the instance family of the Auto Scaling group that the batch job uses. This option is not optimal because it requires a commitment to a consistent amount of compute usage per hour for a one-year term, regardless of the instance type, size, region, or operating system. This can limit the flexibility and scalability of the Auto Scaling group and result in overpaying for unused compute capacity. Moreover, Savings Plans do not provide a capacity reservation, which means the company still needs to reserve capacity with On-Demand Capacity Reservations and pay lower prices with Savings Plans.

B. Purchase a 1-year Reserved Instance for the specific instance type and operating system of the instances in the Auto Scaling group that the batch job uses. This option is not ideal because it requires a commitment to a specific instance configuration for a one-year term, which can reduce the flexibility and scalability of the Auto Scaling group and result in overpaying for unused compute capacity. Moreover, Reserved Instances do not provide a capacity reservation, which means the company still needs to reserve capacity with On-Demand Capacity Reservations and pay lower prices with Reserved Instances.

D. Create a new launch template for the Auto Scaling group Increase the instance size Set a policy to scale out based on CPU usage. This option is not cost-effective because it does not take advantage of the lower prices of Spot Instances. Increasing the instance size can improve the performance of the batch jobs, but it can also increase the cost of the On-Demand instances. Moreover, scaling out based on CPU usage can result in launching more instances than needed, which can also increase the cost of the system.

References:

• 1 Spot Instances - Amazon Elastic Compute Cloud
• 2 Launch templates - Amazon Elastic Compute Cloud
• 3 Auto Scaling groups - Amazon EC2 Auto Scaling
• [4] Savings Plans - Amazon EC2 Reserved Instances and Other AWS Reservation Models

This solution will meet the requirements with the least operational overhead as it uses Amazon Kinesis Data Firehose, which is a fully managed service that can automatically handle the data collection, data transformation, encryption, and data storage in near-real time. Kinesis Data Firehose can automatically store the data in Amazon S3 in Apache Parquet format for further processing. Additionally, it allows you to create an Amazon Kinesis Data Analytics application to analyze the data in near real-time, with no need to manage any infrastructure or invoke any Lambda function. This way you can process a large amount of data with the least operational overhead.

Amazon S3 Glacier is a storage class that provides secure, durable, and extremely low-cost storage for data archiving and long-term backup. It is designed for data that is rarely accessed and for which retrieval times of several hours are suitable1. By storing the video archives in Amazon S3 Glacier, the solution can minimize costs.

Amazon S3 Glacier offers three options for data retrieval: Expedited, Standard, and Bulk. Expedited retrievals typically return data in 1–5 minutes and are suitable for Active Archive use cases. Standard retrievals typically complete within 3–5 hours and are suitable for less urgent needs. Bulk retrievals typically complete within 5–12 hours and are the lowest-cost retrieval option2. By using Expedited retrievals, the solution can meet the requirement of restoring the files in a maximum of five minutes.

B. Store the video archives in Amazon S3 Glacier and use Standard retrievals. This solution will not meet the requirement of restoring the files in a maximum of five minutes, as Standard retrievals typically complete within 3–5 hours.

C. Store the video archives in Amazon S3 Standard-Infrequent Access (S3 Standard-IA). This solution will not meet the requirement of minimizing costs, as S3 Standard-IA is a storage class that provides low-cost storage for data that is accessed less frequently but requires rapid access when needed. It has a higher storage cost than S3 Glacier.

D. Store the video archives in Amazon S3 One Zone-Infrequent Access (S3 One Zone-IA). This solution will not meet the requirement of minimizing costs, as S3 One Zone-IA is a storage class that provides low-cost storage for data that is accessed less frequently but requires rapid access when needed. It has a higher storage cost than S3 Glacier.

Reference URL: https://aws.amazon.com/s3/glacier/

An edge-optimized API endpoint is best for geographically distributed clients, as it routes the API requests to the nearest CloudFront Point of Presence (POP). This reduces the latency and improves the performance of the API. Edge-optimized endpoints are the default type for API Gateway REST APIs1.

A regional API endpoint is intended for clients in the same region as the API, and it does not use CloudFront to route the requests. A private API endpoint is an API endpoint that can only be accessed from a VPC using an interface VPC endpoint. A regional or private endpoint would not meet the requirement of reducing the latency for geographically distributed users1.

An edge-optimized API Gateway is a way to create RESTful APIs that can access multiple DynamoDB tables through AWS Lambda functions. The edge-optimized API Gateway provides low latency and high performance by caching API responses at CloudFront edge locations. The AWS Lambda functions can use the AWS SDK to query or scan the DynamoDB tables and return the data to the API Gateway. This solution meets all the requirements of the question, while the other options do not. References:

• https://aws.amazon.com/blogs/compute/understanding-database-options-for-your-serverless-web-applications/
• https://aws.amazon.com/getting-started/hands-on/build-serverless-web-app-lambda-apigateway-s3-dynamodb-cognito/module-3/
• https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/best-practices.html

This option is the most secure and simple way to encrypt the secrets that are stored in Amazon EKS. AWS Key Management Service (AWS KMS) is a service that allows you to create and manage encryption keys that can be used to encrypt your data. Amazon EKS KMS secrets encryption is a feature that enables you to use a KMS key to encrypt the secrets that are stored in the Kubernetes etcd key-value store. This provides an additional layer of protection for your sensitive data, such as passwords, tokens, and keys. You can create a new KMS key or use an existing one, and then enable the Amazon EKS KMS secrets encryption on the Amazon EKS cluster. You can also use IAM policies to control who can access or use the KMS key.

Option A is not correct because using AWS Secrets Manager to manage, rotate, and store all secrets in Amazon EKS is not necessary or efficient. AWS Secrets Manager is a service that helps you securely store, retrieve, and rotate your secrets, such as database credentials, API keys, and passwords. You can use it to manage secrets that are used by your applications or services outside of Amazon EKS, but it is not designed to encrypt the secrets that are stored in the Kubernetes etcd key-value store. Moreover, using AWS Secrets Manager would incur additional costs and complexity, and it would not leverage the native Kubernetes secrets management capabilities.

Option C is not correct because using the Amazon EBS Container Storage Interface (CSI) driver as an add-on does not encrypt the secrets that are stored in Amazon EKS. The Amazon EBS CSI driver is a plugin that allows you to use Amazon EBS volumes as persistent storage for your Kubernetes pods. It is useful for providing durable and scalable storage for your applications, but it does not affect the encryption of the secrets that are stored in the Kubernetes etcd key-value store. Moreover, using the Amazon EBS CSI driver would require additional configuration and resources, and it would not provide the same level of security as using a KMS key.

Option D is not correct because creating a new AWS KMS key with the alias aws/ebs and enabling default Amazon EBS volume encryption for the account does not encrypt the secrets that are stored in Amazon EKS. The alias aws/ebs is a reserved alias that is used by AWS to create a default KMS key for your account. This key is used to encrypt the Amazon EBS volumes that are created in your account, unless you specify a different KMS key. Enabling default Amazon EBS volume encryption for the account is a setting that ensures that all new Amazon EBS volumes are encrypted by default. However, these features do not affect the encryption of the secrets that are stored in the Kubernetes etcd key-value store. Moreover, using the default KMS key or the default encryption setting would not provide the same level of control and security as using a custom KMS key and enabling the Amazon EKS KMS secrets encryption feature. References:

• Encrypting secrets used in Amazon EKS
• What Is AWS Key Management Service?
• What Is AWS Secrets Manager?
• Amazon EBS CSI driver
• Encryption at rest

A write-through caching strategy adds or updates data in the cache whenever data is written to the database. This ensures that the data in the cache is always consistent with the data in the database. A write-through caching strategy also reduces the cache miss penalty, as data is always available in the cache when it is requested. However, a write-through caching strategy can increase the write latency, as data has to be written to both the cache and the database. A write-through caching strategy is suitable for applications that require high data consistency and low read latency.

A lazy loading caching strategy only loads data into the cache when it is requested, and updates the cache when there is a cache miss. This can result in stale data in the cache, as data is not updated in the cache when it is changed in the database. A lazy loading caching strategy is suitable for applications that can tolerate some data inconsistency and have a low cache miss rate.

An adding TTL caching strategy assigns a time-to-live (TTL) value to each data item in the cache, and removes the data from the cache when the TTL expires. This can help prevent stale data in the cache, as data is periodically refreshed from the database. However, an adding TTL caching strategy can also increase the cache miss rate, as data can be evicted from the cache before it is requested. An adding TTL caching strategy is suitable for applications that have a high cache hit rate and can tolerate some data inconsistency.

An AWS AppConfig caching strategy is not a valid option, as AWS AppConfig is a service that enables customers to quickly deploy validated configurations to applications of any size and scale. AWS AppConfig does not provide a caching layer for web applications.

References: Caching strategies - Amazon ElastiCache, Caching for high-volume workloads with Amazon ElastiCache

Amazon Macie can also send its findings to Amazon EventBridge, which is a serverless event bus that makes it easy to connect applications using data from a variety of sources. You can create an EventBridge rule that filters the SensitiveData event type from Macie findings and sends an Amazon SNS notification to the security team. Amazon SNS is a fully managed messaging service that enables you to send messages to subscribers or other applications. References: https://docs.aws.amazon.com/macie/latest/userguide/macie-findings.html#macie-findings-eventbridge

AWS CloudFormation is a service that helps you model and set up your AWS resources by using templates that describe all the resources that you want, such as Auto Scaling groups, load balancers, and databases. You can use AWS CloudFormation to deploy your infrastructure in an automated and consistent way across multiple environments and regions. You can also use AWS CloudFormation to update or delete your infrastructure as a single unit.

Reference URLs:

1 https://aws.amazon.com/cloudformation/

2 https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/Welcome.html

3 https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/cfn-whatis-concepts.html

it allows the company to deploy an application that processes large quantities of data in parallel and prevent groups of nodes from sharing the same underlying hardware. By running the EC2 instances in a spread placement group, the company can launch a small number of instances across distinct underlying hardware to reduce correlated failures. A spread placement group ensures that each instance is isolated from each other at the rack level. References:

• Placement Groups
• Spread Placement Groups

https://aws.amazon.com/blogs/compute/microservice-delivery-with-amazon-ecs-and-application-load-balancers/

https://aws.amazon.com/cn/blogs/compute/cost-optimization-and-resilience-eks-with-spot-instances/

Option B suggests using an EC2-backed Amazon Machine Image (AMI) lifecycle policy to automate the backup process. By configuring the policy to run twice daily and specifying the copy to the us-west-2 Region, the company can ensure regular backups are created and copied to the alternate region. Option D proposes using AWS Backup, which provides a centralized backup management solution. By creating a backup vault and backup plan based on tag values, the company can automate the backup process for the EC2 instances. The backup schedule can be set to run twice daily, and the destination for the copy can be defined as the us-west-2 Region.

Both options automate the backup process and include copying the backups to the us-west-2 Region, ensuring data resilience in the event of a disaster. These solutions minimize administrative effort by leveraging automated backup and copy mechanisms provided by AWS services.

Amazon EFS is a fully managed, elastic, and scalable file system that can be shared between multiple containers. It provides high availability and fault tolerance by replicating data across multiple Availability Zones. Amazon EFS is compatible with Amazon EKS and AWS Fargate, and can be registered in a StorageClass object on an EKS cluster. Amazon EBS volumes are not supported by AWS Fargate, and cannot be shared between multiple containers without using EBS Multi-Attach, which has limitations and performance implications. EBS Multi-Attach also requires the volumes to be in the same Availability Zone as the worker nodes, which reduces availability and fault tolerance. Synchronizing data between multiple EFS file systems using AWS Lambda is unnecessary, complex, and prone to errors. References:

• Amazon EFS Storage Classes
• Amazon EKS Storage Classes
• Amazon EBS Multi-Attach

AWS Snowball is a petabyte-scale data transport service that uses secure devices to transfer large amounts of data into and out of the AWS Cloud. Snowball addresses common challenges with large-scale data transfers including high network costs, long transfer times, and security concerns. AWS Snowball can transfer up to 80 TB of data per device, and multiple devices can be used in parallel to meet the migration deadline. AWS Snowball is more cost-effective than AWS Snowmobile, which is designed for exabyte-scale data transfers, or Amazon S3 Transfer Acceleration, which is optimized for fast transfers over long distances. Amazon S3 VPC endpoint does not increase the upload speed, but only provides a secure and private connection between the VPC and S3. References: AWS Snowball, AWS Snowmobile, Amazon S3 Transfer Acceleration, Amazon S3 VPC endpoint

Amazon Macie is a fully managed data security and data privacy service that uses machine learning and pattern matching to discover and protect your sensitive data in AWS. Macie can run data discovery jobs that use managed identifiers for various types of PII or financial information, such as passport numbers and credit card numbers. Macie can also generate findings that alert you to potential issues or risks with your data. References: https://docs.aws.amazon.com/macie/latest/userguide/macie-id entifiers.html

This approach will provide both high availability and scalability for the website platform. By moving the database to Amazon Aurora with a read replica in another availability zone, it will provide a failover option for the database. The use of an Application Load Balancer and an Auto Scaling group across two availability zones allows for automatic scaling of the website to meet increased user demand. Additionally, creating an AMI from the original EC2 instance allows for easy replication of the instance in case of failure.

https://docs.aws.amazon.com/AWSEC2/latest/WindowsGuide/monitor -ami-events.html#:~:text=For%20example%2C%20you%20can%20create%20an%20EventBridge%20rule%20that%20detects%20when%20the%20AMI%20creation%20process%20has%20completed%20and%20then%20invokes%20an%20Amazon%20SNS%20topic%20to%20send%20an%20email%20notification%20to%20you.

Creating an Amazon EventBridge (Amazon CloudWatch Events) rule for the CreateImage API call and configuring the target as an Amazon Simple Notification Service (Amazon SNS) topic to send an alert when a CreateImage API call is detected will meet the requirements with the least operational overhead. Amazon EventBridge is a serverless event bus that makes it easy to connect applications together using data from your own applications, integrated Software as a Service (SaaS) applications, and AWS services. By creating an EventBridge rule for the CreateImage API call, the company can set up alerts whenever this operation is called within their account. The alert can be sent to an SNS topic, which can then be configured to send notifications to the company's email or other desired destination.

High availability can be enabled for this architecture quite simply by modifying the existing Auto Scaling group to use multiple availability zones. The ASG will automatically balance the load so you don't actually need to specify the instances per AZ.

https://computingforgeeks.com/stream-logs-in-aws-from-cloudwatch-to-elasticsearch/

Migrate the database to Amazon Aurora MySQL - this will let the DB scale on it's own; it'll scale automatically without needing adjustment. Create AMI of the web app and using a launch template - this will make the creating of any future instances of the app seamless. They can then be added to the auto scaling group which will save them money as it will scale up and down based on demand. Using a spot fleet to launch instances- This solves the "MOST cost-effective" portion of the question as spot instances come at a huge discount at the cost of being terminated at any time Amazon deems fit. I think this is why there's a bit of disagreement on this. While it's the most cost effective, it would be a terrible choice if amazon were to terminate that spot instance during a busy period.

These are some of the main use cases for AWS DataSync: • Data migration – Move active datasets rapidly over the network into Amazon S3, Amazon EFS, or FSx for Windows File Server. DataSync includes automatic encryption and data integrity validation to help make sure that your data arrives securely, intact, and ready to use.

"DataSync includes encryption and integrity validation to help make sure your data arrives securely, intact, and ready to use." https://aws.amazon.com/datasync/faqs/

This is the purpose of bookmarks: "AWS Glue tracks data that has already been processed during a previous run of an ETL job by persisting state information from the job run. This persisted state information is called a job bookmark. Job bookmarks help AWS Glue maintain state information and prevent the reprocessing of old data." https://docs.aws.amazon.com/glue/latest/dg/monitor-continuations.html

"Create an Amazon SQS queue to hold the jobs that needs to be processed. Create an Amazon EC2 Auto Scaling group for the compute application. Set the scaling policy for the Auto Scaling group to add and remove nodes based on the number of items in the SQS queue"

In this case we need to find a durable and loosely coupled solution for storing jobs. Amazon SQS is ideal for this use case and can be configured to use dynamic scaling based on the number of jobs waiting in the queue.To configure this scaling you can use the backlog per instance metric with the target value being the acceptable backlog per instance to maintain. You can calculate these numbers as follows: Backlog per instance: To calculate your backlog per instance, start with the ApproximateNumberOfMessages queue attribute to determine the length of the SQS queue

https://docs.aws.amazon.com/AWSEC2/latest/WindowsGuide/AmazonEFS.html Amazon FSx for Windows File Server provides fully managed Microsoft Windows file servers, backed by a fully native Windows file system. https://docs.aws.amazon.com/fsx/latest/WindowsGuide/what-is.html

(https://aws.amazon.com/cloudfront

(https://aws.amazon.com/blogs/security/how-to-restrict-amazon-s3-bucket-access-to-a-specific-iam-role/ )

You can use CloudFront to deliver video on demand (VOD) or live streaming video using any HTTP origin. One way you can set up video workflows in the cloud is by using CloudFront together with AWS Media Services. https://docs.aws.am azon.com/AmazonCloudFront/latest/DeveloperGuide/on-demand-streaming-video.html

The details are revealed in below url: https://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSDeveloperGuide/FIFO-queues.html

FIFO (First-In-First-Out) queues are designed to enhance messaging between applications when the order of operations and events is critical, or where duplicates can't be tolerated. Examples of situations where you might use FIFO queues include the following: To make sure that user-entered commands are run in the right order. To display the correct product price by sending price modifications in the right order. To prevent a student from enrolling in a course before registering for an account.

AWS Fargate is a serverless experience for user applications, allowing the user to concentrate on building applications instead of configuring and managing servers. Fargate also automates resource management, allowing users to easily scale their applications in response to demand.

https://aws.amazon.com/blogs/compute/building-loosely-coupled-scalable-c-applications- with-amazon-sqs-and-amazon-sns/

https://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSDeveloperGuide/sqs-dead-letter-queues.html

Amazon Aurora is a fully managed, scalable, and highly available relational database service that is compatible with PostgreSQL. Migrating the database to Amazon Aurora would reduce the operational overhead of maintaining the database infrastructure and allow the company to focus on building and scaling the application. AWS Fargate is a fully managed container orchestration service that enables users to run containers without the need to manage the underlying EC2 instances. By using AWS Fargate with Amazon Elastic Container Service (Amazon ECS), the solutions architect can improve the scalability and efficiency of the web application and reduce the operational overhead of maintaining the underlying infrastructure.

https://docs.aws.amazon.com/lambda/latest/dg/configuration-vpc.html#vpc-managing-eni

Cloudfront for rapid response and s3 to minimize infrastructure.

https://aws.amazon.com/blogs/big-data/top-10-performance-tuning-tips-for-amazon-athena/

This solution meets the requirements of measuring the effectiveness of marketing campaigns by performing batch processing on csv files of sales data and storing the results in an Amazon S3 bucket once every hour. An AWS duo ETL process can use services such as AWS Glue or AWS Data Pipeline to extract data from S3, transform it into a more efficient format such as Apache Parquet, and load it back into S3. Apache Parquet is a columnar storage format that can improve the query performance and reliability of Athena by reducing the amount of data scanned, improving compression ratio, and enabling predicate pushdown.

https://aws.amazon.com/global-accelerator/faqs/

HTTP /HTTPS - ALB ; TCP and UDP - NLB; Lowest latency routing and more throughput. Also supports failover, uses Anycast Ip addressing - Global Accelerator Caching at Egde Locations – Cloutfront

WS Global Accelerator automatically checks the health of your applications and routes user traffic only to healthy application endpoints. If the health status changes or you make configuration updates, AWS Global Accelerator reacts instantaneously to route your users to the next available endpoint..

https://docs.aws.amazon.com/autoscaling/application/userguide/application-auto-scaling-target-tracking.html

EC2 instance Savings Plan saves 72% while Compute Savings Plans saves 66%. But according to link, it says "Compute Savings Plans provide the most flexibility and help to reduce your costs by up to 66%. These plans automatically apply to EC2 instance usage regardless of instance family, size, AZ, region, OS or tenancy, and also apply to Fargate and Lambda usage." EC2 instance Savings Plans are not applied to Fargate or Lambda

https://aws.amazon.com/kms/faqs/ #:~:text=If%20you%20are%20a%20developer%20who%20needs%20to%20digitally,a%20broad%20set%20of%20industry%20and%20regional%20compliance%20regimes.

ElastiCache, enhances the performance of web applications by quickly retrieving information from fully-managed in-memory data stores. It utilizes Memcached and Redis, and manages to considerably reduce the time your applications would, otherwise, take to read data from disk-based databases. Amazon CloudFront supports dynamic content from HTTP and WebSocket protocols, which are based on the Transmission Control Protocol (TCP) protocol. Common use cases include dynamic API calls, web pages and web applications, as well as an application's static files such as audio and images. It also supports on-demand media streaming over HTTP. AWS Global Accelerator supports both User Datagram Protocol (UDP) and TCP-based protocols. It is commonly used for non-HTTP use cases, such as gaming, IoT and voice over IP. It is also good for HTTP use cases that need static IP addresses or fast regional failover

Service control policies (SCPs) are one type of policy that you can use to manage your organization. SCPs offer central control over the maximum available permissions for all accounts in your organization, allowing you to ensure your accounts stay within your organization's access control guidelines. See https://docs.aws.amazon.com/organizations/latest/userguide/orgs_manage_policies_scp.html.

https://aws.amazon.com/p remiumsupport/knowledge-center/cloudwatch-memory-metrics-ec2/

By using an SQS queue and Lambda, the solutions architect can decouple the API front end from the processing microservices and improve the overall scalability and availability of the system. The SQS queue acts as a buffer, allowing the API front end to continue accepting user requests even if the processing microservices are experiencing high workloads or are temporarily unavailable. The Lambda function can then retrieve requests from the SQS queue and write them to DynamoDB, ensuring that all user requests are stored and processed. This approach allows the company to scale the processing microservices independently from the API front end, ensuring that the API remains available to users even during periods of high demand.

This solution meets the requirements of a two-tier application that has a variable demand based on the time of day and must be available at all times, while minimizing the overall cost. EC2 Reserved Instances can provide significant savings compared to On-Demand Instances for the baseline level of usage, and they can guarantee capacity reservation when needed. EC2 Spot Instances can provide up to 90% savings compared to On-Demand Instances for any additional capacity that the application needs during peak hours. Spot Instances are suitable for stateless applications that can tolerate interruptions and can be replaced by other instances. Stopping the RDS database when it is not in use can reduce the cost of running the database tier.

Option A is incorrect because using all EC2 Spot Instances can affect the availability of the application if there are not enough spare capacity or if the Spot price exceeds the maximum price. Stopping the RDS database when it is not in use can reduce the cost of running the database tier, but it can also affect the availability of the application. Option B is incorrect because purchasing EC2 Instance Savings Plans to cover five EC2 instances can lock in a fixed amount of compute usage per hour, which may not match the actual usage pattern of the application. Purchasing an RDS Reserved DB Instance can provide savings for the database tier, but it does not allow stopping the database when it is not in use. Option D is incorrect because purchasing EC2 Instance Savings Plans to cover two EC2 instances can lock in a fixed amount of compute usage per hour, which may not match the actual usage pattern of the application. Using up to three additional EC2 On-Demand Instances as needed can incur higher costs than using Spot Instances.

References:

• https://aws.amazon.com/ec2/pricing/reserved-instances/
• https://aws.amazon.com/ec2/spot/
• https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/USER_StopInstance.html

Queue has Limited throughput (300 msg/s without batching, 3000 msg/s with batching whereby up-to 10 msg per batch operation; Msg duplicates not allowed in the queue (exactly-once delivery); Msg order is preserved (FIFO); Queue name must end with .fifo

This solution meets the requirements of a customer-facing application that has a clearly defined access pattern throughout the year and a variable number of reads and writes that depend on the time of year. Amazon DynamoDB is a fully managed NoSQL database service that can handle any level of request traffic and data size. DynamoDB auto scaling can automatically adjust the provisioned read and write capacity based on the actual workload. DynamoDB on-demand backups can create full backups of the tables for data protection and archival purposes. DynamoDB Streams can capture a time-ordered sequence of item-level modifications in the tables for audit purposes.

Option B is incorrect because Amazon Redshift is a data warehouse service that is designed for analytical workloads, not for customer-facing applications. Option C is incorrect because Amazon RDS with Provisioned IOPS can provide consistent performance for relational databases, but it may not be able to handle unpredictable spikes in traffic and data size. Option D is incorrect because Amazon Aurora MySQL with auto scaling can provide high performance and availability for relational databases, but it does not support audit logging as a parameter.

References:

• https://aws.amazon.com/dynamodb/
• https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/AutoScaling.html
• https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/BackupRestore.html
• https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/Streams.html

https://docs.aws.amazon.com/filegateway/latest/filefsxw/what-is-file-fsxw.html

To meet the requirements of the company to have access to both AWS and on-premises file storage with minimum latency, a hybrid cloud architecture can be used. One solution is to deploy and configure Amazon FSx for Windows File Server on AWS, which provides fully managed Windows file servers. The on-premises file data can be moved to the FSx File Gateway, which can act as a bridge between on-premises and AWS file storage. The cloud workloads can be configured to use FSx for Windows File Server on AWS, while the on-premises workloads can be configured to use the FSx File Gateway. This solution minimizes operational overhead and requires no significant changes to the existing file access patterns. The connectivity between on-premises and AWS can be established using an AWS Site-to-Site VPN connection.

https://aws.amazon.com/getting-started/hands-on/build-serverless-web-app-lambda-apigateway-s3-dynamodb-cognito/module-4/

Build a Serverless Web Application with AWS Lambda, Amazon API Gateway, AWS Amplify, Amazon DynamoDB, and Amazon Cognito. This example showed similar setup as question: Build a Serverless Web Application with AWS Lambda, Amazon API Gateway, AWS Amplify, Amazon DynamoDB, and Amazon Cognito

Amazon Athena can be used to query JSON in S3

https://docs.aws.a mazon.com/kms/latest/developerguide/rotate-keys.html

When you enable automatic key rotation for a customer managed key, AWS KMS generates new cryptographic material for the KMS key every year. AWS KMS also saves the KMS key's older cryptographic material in perpetuity so it can be used to decrypt data that the KMS key encrypted.

Key rotation in AWS KMS is a cryptographic best practice that is designed to be transparent and easy to use. AWS KMS supports optional automatic key rotation only for customer managed CMKs. Enable and disable key rotation. Automatic key rotation is disabled by default on customer managed CMKs. When you enable (or re-enable) key rotation, AWS KMS automatically rotates the CMK 365 days after the enable date and every 365 days thereafter.

This solution meets the following requirements:

• It is cost-effective, as it only uses serverless components that are charged based on usage and do not require any upfront provisioning or maintenance.
• It is scalable, as it can handle any number of recipe records that are uploaded to the S3 bucket without any performance degradation or manual intervention.
• It is easy to implement, as it does not require any machine learning knowledge or complex data processing logic. Amazon Comprehend is a natural language processing service that can automatically extract entities such as ingredients from text files. The Lambda function can simply invoke the Comprehend API and store the results in the DynamoDB table.
• It is reliable, as it can handle non-food records and errors gracefully. Amazon Comprehend can detect the language and domain of the text files and return an appropriate response. The Lambda function can also implement error handling and logging mechanisms to ensure the data quality and integrity.

References:

• Using AWS Lambda with Amazon S3 - AWS Lambda
• What Is Amazon Comprehend? - Amazon Comprehend
• Working with Tables - Amazon DynamoDB

AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers. You can use Lambda to create and test microservices that are written in Python or other supported languages. Lambda scales automatically to handle the number of requests per second. You only pay for the compute time you consume. Lambda also integrates with other AWS services, such as Amazon API Gateway, Amazon S3, Amazon DynamoDB, and Amazon SQS, to enable event-driven architectures. Lambda has minimal infrastructure and operational overhead, as you do not need to manage servers, operating systems, patches, or scaling policies.

The other options are not serverless solutions and require more infrastructure and operational support. They also do not scale automatically to handle the number of requests per second. A Spot Fleet is a collection of EC2 instances that run on spare capacity at low prices. However, Spot Instances can be interrupted by AWS at any time, which can affect the availability and performance of your microservice. AWS Elastic Beanstalk is a service that automates the deployment and management of web applications on EC2 instances. However, you still need to provision, configure, and monitor the underlying EC2 instances and load balancers. Amazon EKS is a service that runs Kubernetes on AWS. However, you still need to create, configure, and manage the EC2 instances that form the Kubernetes cluster and nodes. You also need to install and update the Kubernetes software and tools.

References:

• What is AWS Lambda?
• Building Lambda functions with Python
• Create a layer for a Lambda Python function
• AWS Lambda – Function in Python
• How do I call my AWS Lambda function from a local python script?

The best solution for this situation is option B, setting up AWS Global Accelerator with UDP listeners and endpoint groups in each Region. AWS Global Accelerator is a networking service that improves the availability and performance of internet applications by routing user requests to the nearest AWS Region [1]. It also improves the performance of UDP applications by providing faster, more reliable data transfers with lower latency and fewer packet losses. By setting up UDP listeners and endpoint groups in each Region, Global Accelerator will route traffic to the nearest Region for faster response times and a better user experience.

To store and access files that are 25 GB or larger across many EC2 instances and across multiple Availability Zones, Amazon Elastic File System (Amazon EFS) is a suitable solution. Amazon EFS provides a simple, scalable, elastic file system that can be mounted on multiple EC2 instances concurrently. Amazon EFS supports high availability and durability by storing data across multiple Availability Zones within a Region.

References:

• What Is Amazon Elastic File System?
• Using EFS with EC2

This solution meets the requirements of moving the files automatically, running Lambda functions on the copied data, and sending the data files to SageMaker Pipelines with the least operational overhead. S3 replication can copy the files from the initial S3 bucket to the analysis S3 bucket as they arrive. The analysis S3 bucket can send event notifications to Amazon EventBridge (Amazon CloudWatch Events) when an object is created. EventBridge can trigger Lambda and SageMaker Pipelines as targets for the ObjectCreated rule. Lambda can run pattern-matching code on the copied data, and SageMaker Pipelines can execute a pipeline with the data files.

Option A is incorrect because creating a Lambda function to copy the files to the analysis S3 bucket is not necessary when S3 replication can do that automatically. It also adds operational overhead to manage the Lambda function. Option B is incorrect because creating a Lambda function to copy the files to the analysis S3 bucket is not necessary when S3 replication can do that automatically. It also adds operational overhead to manage the Lambda function. Option C is incorrect because using S3 event notification with multiple destinations can result in throttling or delivery failures if there are too many events.

References:

• https://aws.amazon.com/blogs/machine-learning/automate-feature-engineering-pipelines-with-amazon-sagemaker/
• https://docs.aws.amazon.com/sagemaker/latest/dg/automating-sagemaker-with-eventbridge.html
• https://aws.amazon.com/about-aws/whats-new/2021/04/new-options-trigger-amazon-sagemaker-pipeline-executions/

In compliance mode, a protected object version can't be overwritten or deleted by any user, including the root user in your AWS account. When an object is locked in compliance mode, its retention mode can't be changed, and its retention period can't be shortened. Compliance mode helps ensure that an object version can't be overwritten or deleted for the duration of the retention period. In governance mode, users can't overwrite or delete an object version or alter its lock settings unless they have special permissions. With governance mode, you protect objects against being deleted by most users, but you can still grant some users permission to alter the retention settings or delete the object if necessary. In Governance mode, Objects can be deleted by some users with special permissions, this is against the requirement.

Compliance:

- Object versions can't be overwritten or deleted by any user, including the root user

- Objects retention modes can't be changed, and retention periods can't be shortened

Governance:

- Most users can't overwrite or delete an object version or alter its lock settings

- Some users have special permissions to change the retention or delete the object

https://docs.aws.amazon.com/sns/latest/dg/sns-dead-letter-queues.html

https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-ec2-placementgroup.html

"A cluster placement group is a logical grouping of instances within a single Availability Zone that benefit from low network latency, high network throughput"

CloudFront uses a local cache to provide the response, AWS Global accelerator proxies requests and connects to the application all the time for the response.

https://docs.aws.amazon.com/AmazonCloudFron t/latest/DeveloperGuide/private-content-restricting-access-to-s3.html#private-content-granting-permissions-to-oai

https://aws.amazo n.com/premiumsupport/knowledge-center/cloudfront-access-to-amazon-s3/

https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/private-content-restricting-access-to-s3.html#private-content-restricting-access-to-s3-overview

https://docs.aws.amazon.com/rekognition/latest/dg/moderation.html?pg=ln &sec=ft

https://docs.aws.amazon.com/rekognition/latest/dg/a2i-rekognition.html

https://docs.aws.amazon.com/eventbridge/latest/userguide/resource-based-policies-eventbridge.html#l ambda-permissions

https://aws.amazon.com/premiumsupport/knowledge-center/elb-redirect-http-to-https-using-alb/

How can I redirect HTTP requests to HTTPS using an Application Load Balancer? Last updated: 2020-10-30 I want to redirect HTTP requests to HTTPS using Application Load Balancer listener rules. How can I do this? Resolution Reference: https://aws.amazon.com/premiumsupport/knowledge-center/elb-redirect-http-to-https-using-alb/

 AWS Snowball Edge is a type of Snowball device with on-board storage and compute power for select AWS capabilities. Snowball Edge can do local processing and edge-computing workloads in addition to transferring data between your local environment and the AWS Cloud1. Users can order an AWS Snowball Edge Storage Optimized device that includes Amazon EC2 compute to move 50 TB of data from on premises to AWS. The Storage Optimized device has 80 TB of usable storage and 40 vCPUs of compute power2. Users can copy the data to the device using the AWS OpsHub graphical user interface or the Snowball client command line tool3. Users can also create and run Amazon EC2 instances on the device using Amazon Machine Images (AMIs) that are compatible with the sbe1 instance type. Users can use the Snowball Edge device to transfer the data and run the transformation job locally without using any network bandwidth.

Users can also create a new EC2 instance on AWS to run the transformation application after the data transfer is complete. Amazon EC2 is a web service that provides secure, resizable compute capacity in the cloud. Users can launch an EC2 instance in the same AWS Region where they send their Snowball Edge device and choose an AMI that matches their application requirements. Users can use the EC2 instance to continue running the transformation job in the AWS Cloud.

Amazon CloudFront is a content delivery network (CDN) that speeds up the delivery of static and dynamic web content, such as HTML pages, images, and videos. By using CloudFront, the HTML pages will be served to users from the edge location that is closest to them, resulting in faster delivery and a better user experience. CloudFront can also handle the high traffic and large number of requests expected for the global event, ensuring that the HTML pages are available and accessible to users around the world.

Using AWS WAF has several benefits. Additional protection against web attacks using criteria that you specify. You can define criteria using characteristics of web requests such as the following: Presence of SQL code that is likely to be malicious (known as SQL injection). Presence of a script that is likely to be malicious (known as cross-site scripting). AWS Firewall Manager simplifies your administration and maintenance tasks across multiple accounts and resources for a variety of protections. https://docs.aws.amazon.com/waf/latest/developerguide/what-is-aws-waf.html

Application availability: NLB cannot assure the availability of the application. This is because it bases its decisions solely on network and TCP-layer variables and has no awareness of the application at all. Generally, NLB determines availability based on the ability of a server to respond to ICMP ping or to correctly complete the three-way TCP handshake. ALB goes much deeper and is capable of determining availability based on not only a successful HTTP GET of a particular page but also the verification that the content is as was expected based on the input parameters.

https://aws.amazon.com/ step-functions/#:~:text=AWS%20Step%20Functions%20is%20a,machine%20learning%20(ML)%20pipelines.

"A common use case for AWS Step Functions is a task that requires human intervention (for example, an approval process). Step Functions makes it easy to coordinate the components of distributed applications as a series of steps in a visual workflow called a state machine. You can quickly build and run state machines to execute the steps of your application in a reliable and scalable fashion. (https://aws.amazon.com/pt/blogs/compute/implementing-serverless-manual-approval-steps-in-aws-step-functions-and-amazon-api-gate way/)"

The best option to improve the security of the data in transit is to configure a TLS listener and deploy the server certificate on the NLB. This will ensure that the data is encrypted and secure as it travels through the network. Additionally, you could also configure AWS Shield Advanced and enable AWS WAF on the NLB to further protect the network from malicious attacks. Alternatively, you could also change the load balancer to an Application Load Balancer (ALB) and enable AWS WAF on the ALB. Finally, you could also encrypt the Amazon Elastic Block Store (Amazon EBS) volume on the EC2 instances by using AWS Key Management Service (AWS KMS).

You must specify an SSL certificate for a TLS listener. The load balancer uses the certificate to terminate the connection and decrypt requests from clients before routing them to targets. https://docs.aws.amazon.com/elasticloadbalancing/latest/network/create-listener.html

 Security groups are virtual firewalls that protect AWS instances and can be applied to EC2, ELB and RDS1. Security groups have rules for inbound and outbound traffic and are stateful, meaning that responses to allowed inbound traffic are allowed to flow out of the instance2. Network ACLs are different from security groups in several ways. They cover entire subnets, not individual instances, and are stateless, meaning that they require rules for both inbound and outbound traffic2. Network ACLs also support deny rules, while security groups only support allow rules2.

To meet the requirements of the scenario, the solutions architect should create two security groups: one for the DB instance and one for the web servers in the public subnet. The security group for the DB instance should allow traffic from the public subnet CIDR block on port 3306, which is the default port for MySQL3. This way, only the web servers in the public subnet can access the DB instance on that port. The security group for the web servers should allow traffic from 0 0 0 O’O on port 443, which is the default port for HTTPS4. This way, the web servers can accept secure connections from the internet on that port.

EBS encryption by default is a feature that enables encryption for all new EBS volumes and snapshots created in a Region1. EBS encryption by default uses a service managed key or a customer managed key that is stored in AWS KMS1. EBS encryption by default is suitable for scenarios where data at rest must be encrypted by using a customer managed key, such as the digital media streaming application in the scenario1.

To meet the requirements of the scenario, the solutions architect should enable EBS encryption by default in the AWS Region where the EKS cluster will be created. The solutions architect should select the customer managed key as the default key for encryption1. This way, all new EBS volumes and snapshots created in that Region will be encrypted by using the customer managed key.

EKS encryption provider support is a feature that enables envelope encryption of Kubernetes secrets in EKS with a customer managed key that is stored in AWS KMS2. Envelope encryption means that data is encrypted by data encryption keys (DEKs) using AES-GCM; DEKs are encrypted by key encryption keys (KEKs) according to configuration in AWS KMS3. EKS encryption provider support is suitable for scenarios where secrets must be encrypted by using a customer managed key, such as the digital media streaming application in the scenario2.

To meet the requirements of the scenario, the solutions architect should create the EKS cluster and create an IAM role that has a policy that grants permission to the customer managed key. The solutions architect should associate the role with the EKS cluster2. This way, the EKS cluster can use envelope encryption of Kubernetes secrets with the customer managed key.

https://docs.aws.amazon.com/sns/latest/dg/sns-server-side-encryption.html https://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSDeveloperGuide/sqs-server-side-encryption.html

Option A is the most appropriate solution for reducing replication lag without significant changes to the application code and minimizing ongoing operational overhead. Migrating the database to Amazon Aurora MySQL allows for improved replication performance and higher scalability compared to Amazon RDS for MySQL. Aurora Replicas provide faster replication, reducing the replication lag, and Aurora Auto Scaling ensures that there are enough Aurora Replicas to handle the incoming traffic. Additionally, Aurora MySQL native functions can replace the stored procedures, reducing the load on the database and improving performance.

The best option is to use AWS CloudTrail to find the desired information. AWS CloudTrail is a service that enables governance, compliance, operational auditing, and risk auditing of AWS account activities. CloudTrail can be used to log all changes made to resources in an AWS account, including changes made by IAM users, EC2 instances, AWS management console, and other AWS services. By using CloudTrail, the solutions architect can identify the IAM user who made the configuration changes to the security group rules.

UESTION NO: 549

A company has a three-tier environment on AWS that ingests sensor data from its users' devices The traffic flows through a Network Load Balancer (NIB) then to Amazon EC2 instances for the web tier and finally to EC2 instances for the application tier that makes database calls

What should a solutions architect do to improve the security of data in transit to the web tier?

A. Configure a TLS listener and add the server certificate on the NLB

B. Configure AWS Shield Advanced and enable AWS WAF on the NLB

C. Change the load balancer to an Application Load Balancer and attach AWS WAF to it

D. Encrypt the Amazon Elastic Block Store (Amazon EBS) volume on the EC2 instances using AWS Key Management Service (AWS KMS)

Answer: A

A: How do you protect your data in transit?

Best Practices:

Implement secure key and certificate management: Store encryption keys and certificates securely and rotate them at appropriate time intervals while applying strict access control; for example, by using a certificate management service, such as AWS Certificate Manager (ACM).

Enforce encryption in transit: Enforce your defined encryption requirements based on appropriate standards and recommendations to help you meet your organizational, legal, and compliance requirements.

Automate detection of unintended data access: Use tools such as GuardDuty to automatically detect attempts to move data outside of defined boundaries based on data classification level, for example, to detect a trojan that is copying data to an unknown or untrusted network using the DNS protocol.

Authenticate network communications: Verify the identity of communications by using protocols that support authentication, such as Transport Layer Security (TLS) or IPsec.

https://wa.aws.amazon.com/wat.question.SEC_9.en.html