DVA-C02 Sample Questions Answers

Requirement Summary:

DynamoDB table Orders

Partition key: OrderID

No sort key

100,000+ records

Need to efficiently retrieve all records where:

OrderSource = "MobileApp"

Must optimize for user experience (i.e., performance)

Evaluate Options:

Option A: Scan with FilterExpression

Inefficient for large tables

A Scan reads every item in the table and then applies a filter condition.

This is slow, expensive, and not scalable as the dataset grows.

Option B: Create a Local Secondary Index (LSI) with OrderSource

Invalid: LSIs require the same partition key (OrderID) as the base table.

Since OrderSource is not part of the primary key, and we want to query based on it, LSI won’t help.

Option C: Create a GSI with OrderSource as the sort key

Misuse of sort key: Querying by sort key alone is not allowed in DynamoDB.

You must specify a partition key in a Query.

This design is not valid for the use case.

Option D: Create a GSI with OrderSource as the partition key

BEST CHOICE: With a GSI on OrderSource, you can query efficiently for all items where OrderSource = "MobileApp".

DynamoDB GSIs allow an alternative access pattern for queries not supported by the base table schema.

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

Querying a GSI: https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/Query.html

Scan vs Query: https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/bp-query-scan.html

This solution allows the fastest response for the query because it enables the query to use a single partition key value (the Product ID) and a range of sort key values (the Product Rating) to find the matching items. A global secondary index (GSI) is an index that has a partition key and an optional sort key that are different from those on the base table. A GSI can be created at any time and can be queried or scanned independently of the base table. A local secondary index (LSI) is an index that has the same partition key as the base table, but a different sort key. An LSI can only be created when the base table is created and must be queried together with the base table partition key. Using a GSI with Product ID as the partition key and Review ID as the sort key will not allow the query to use a range of sort key values to find the highest ratings. Using an LSI with Product ID as the partition key and Product Rating as the sort key will not work because Product ID is not the partition key of the base table. Using an LSI with Review ID as the partition key and Product ID as the sort key will not allow the query to use a single partition key value to find the matching items.

The correct policy condition ensures that:

The LeadingKeys condition restricts operations to the authenticated user's user_id.

The Attributes condition limits the updatable attributes to user_name.

Explanation of Choices:

Option A: Correctly enforces both the key restriction (dynamodb:LeadingKeys) and ensures only the user_name attribute can be updated.

Option B, C, D: Use incorrect conditions, such as referencing user_name in the LeadingKeys or including other attributes like user_id in updatable fields.

Distributed Tracing with X-Ray: X-Ray helps visualize request paths and identify bottlenecks in applications distributed across Regions.

Region Annotations (Automatic for AWS Services): X-Ray automatically adds a Region annotation to segments representing calls to AWS services. This aids in tracing cross-Region traffic.

Region Annotations (Manual for User-Defined): For segments representing calls to user-defined services in different Regions, the developer needs to add the Region annotation manually to enable comprehensive tracing.

Step-by-Step Breakdown:

Requirement Summary:

AWS AppSync API needs to invoke Lambda functions

Some Lambda functions are long-running

AppSync should return immediately, minimizing operational overhead

**Option A: AppSync + Lambda as data source using Event Mode

Correct: AWS AppSync supports asynchronous (event) invocation of Lambda data sources using Event Mode.

Event Mode means:

AppSync invokes Lambda asynchronously

Immediately returns a response to the client (typically a predefined payload or null)

Ideal for fire-and-forget workloads or when the response is not immediately needed

Option B: Increase Lambda timeout

Incorrect: This keeps AppSync waiting.

Even with increased timeout, synchronous invocations would block AppSync responses.

Option C: SQS queue + polling Lambda

Possible but too complex for this use case.

Requires additional infrastructure: queue + mapping + custom logic.

Higher operational overhead compared to built-in AppSync Event Mode.

Option D: Enable caching in AppSync

Irrelevant: AppSync cache is for optimizing repeated read queries, not for async workflows.

Asynchronous Lambda with AppSync: https://docs.aws.amazon.com/appsync/latest/devguide/resolver-mapping-template-reference-lambda.html#async-lambda-invocation

Lambda as AppSync Data Source: https://docs.aws.amazon.com/appsync/latest/devguide/tutorial-lambda-resolvers.html

Event Mode Docs: https://docs.aws.amazon.com/appsync/latest/devguide/lambda-resolvers.html#event-invocation-mode

SQS Delivery Behavior: Standard SQS queues guarantee at-least-once delivery, meaning messages may be processed more than once. This can lead to duplicate emails in this scenario.

Visibility Timeout: If the visibility timeout on the SQS queue is too short, a message might become visible for another consumer before the first Lambda function finishes processing it. This can also lead to duplicates.

Step Functions Retriers: Retriers provide a built-in way to gracefully handle transient errors within State Machines. Here's how to use them:

Directly attach a retrier to the problematic 'GetResource' task.

Configure the retrier:

ErrorEquals: Set this to ['TooManyRequestsException'] to target the specific error.

IntervalSeconds: Set to 10 for the desired retry delay.

MaxAttempts: Set to 1, as you want only one retry attempt.

Error Handling:

Upon 'TooManyRequestsException', the retrier triggers the task again after 10 seconds.

On a second failure, Step Functions moves to the next state or fails the workflow, as per your design.

'IllegalArgumentException' causes error propagation as intended.

This solution meets the requirements in the most secure way because it uses a service that is integrated with CloudFormation to manage sensitive data in encrypted form. AWS Systems Manager Parameter Store provides secure, hierarchical storage for configuration data management and secrets management. You can store sensitive data as secure string parameters, which are encrypted using an AWS Key Management Service (AWS KMS) key of your choice. You can also use dynamic references in your CloudFormation templates to specify template values that are stored in Parameter Store or Secrets Manager without having to include them in your templates. Dynamic references are resolved only during stack creation or update operations, which reduces exposure risks for sensitive data. Putting sensitive data into a CloudFormation parameter will not encrypt them or protect them from unauthorized access. Putting sensitive data into an Amazon S3 bucket or Amazon Elastic File System (Amazon EFS) will require additional configuration and integration with CloudFormation and may not provide fine-grained access control or encryption for sensitive data.

The solution that will meet the requirements is to add a test phase to the amplify.yml build settings for the application. This way, the developer can run end-to-end tests on every code commit and catch any bugs before deploying to production. The other options either do not support end-to-end testing, or do not run tests automatically.

To rotate database credentials without downtime, Secrets Manager recommends the alternating users (dual user) rotation strategy. With a single-user strategy, Secrets Manager changes the password for the same database user that the application is actively using. If the application continues using cached/old credentials during rotation, authentication failures and downtime can occur.

With the alternating users strategy, two database users exist (often named something like appuser and appuser_clone). Secrets Manager alternates which user is “active” in the secret. During rotation, Secrets Manager updates the inactive user’s password, verifies it, updates the secret to point to the newly updated user, and then (optionally) retires the old credentials. This approach minimizes disruption because there is always one known-good credential set while the other is being updated.

The question specifically requires regular rotation using Secrets Manager. That is achieved by enabling automatic rotation on the secret with a schedule (for example, every 30/60/90 days). Automatic rotation invokes the rotation Lambda (AWS-provided or configured) and performs the workflow without manual intervention.

Option D combines both requirements: automatic rotation plus the alternating users strategy for high availability and no downtime.

Option B mentions alternating users but “managed rotation” is not the standard term used in these choices as the primary differentiator—automatic rotation is the necessary capability to rotate regularly without manual effort.

Options A and C (single user) are more likely to cause downtime during rotation.

Therefore, configure automatic rotation with the alternating users rotation strategy.

https://docs.aws.amazon.com/IAM/latest/UserGuide/reference_policies_examples_lambda-access-dynamodb.html

DynamoDB already supports encryption at rest by default (with AWS owned keys or AWS managed/customer managed KMS keys). However, the requirement here is stronger and explicit: patient data must be encrypted before it is stored in DynamoDB, meaning the encryption must occur client-side / application-side so that the data remains encrypted as it traverses the stack and is still encrypted when written to the table. This is often required for highly sensitive PII/PHI where administrators or downstream systems should not see plaintext.

Option A meets this requirement by encrypting the payload inside the Lambda function before making the PutItem call to DynamoDB, using KMS-backed envelope encryption via the AWS Encryption SDK (the option names it “Database Encryption SDK,” but the intent is client-side encryption). The encrypted fields remain ciphertext in transit to DynamoDB and at rest inside DynamoDB, satisfying both “in transit” and “at rest” with encryption happening prior to storage.

Option B only ensures DynamoDB encryption at rest at the service level, but the data would still be plaintext in the item attributes as seen by the application and anyone with DynamoDB read permissions (even though stored encrypted on disk). It does not guarantee “encrypted before stored.”

Option C encrypting after the write via streams is too late: plaintext would already be stored (and potentially accessed) before post-processing.

Option D adds unnecessary workflow complexity. Encrypting in a queue still requires encryption before DynamoDB, but it does not inherently simplify compared to encrypting directly in the Lambda that writes to DynamoDB.

Therefore, client-side encryption in Lambda using KMS-backed encryption is the correct solution.

Amazon API Gateway is a service that enables developers to create, publish, maintain, monitor, and secure APIs at any scale. The developer can enable API caching in API Gateway to cache responses from the backend integration point for a specified time-to-live (TTL) period. This can improve the responsiveness of the APIs by reducing the number of calls made to the backend service.

This solution will meet the requirements by storing the Root CA Cert as a Secure String parameter in AWS Systems Manager Parameter Store, which is a secure and scalable service for storing and managing configuration data and secrets. The resource-based policy will allow IAM users in different AWS accounts and environments to access the parameter without requiring cross-account roles or permissions. The Lambda code will be refactored to load the Root CA Cert from the parameter store and modify the runtime trust store outside the Lambda function handler, which will improve performance and reduce latency by avoiding repeated calls to Parameter Store and trust store modifications for each invocation of the Lambda function. Option A is not optimal because it will use AWS Secrets Manager instead of AWS Systems Manager Parameter Store, which will incur additional costs and complexity for storing and managing a non-secret configuration data such as Root CA Cert. Option C is not optimal because it will deactivate the application secrets and monitor the application error logs temporarily, which will cause application downtime and potential data loss. Option D is not optimal because it will modify the runtime trust store inside the Lambda function handler, which will degrade performance and increase latency by repeating unnecessary operations for each invocation of the Lambda function.

Comprehensive Detailed and Lengthy Step-by-Step Explanation with All AWS Developer References:

1. Understanding the Use Case:

The company wants to gradually release a new feature to 15% of users to perform testing. AWS AppConfig is designed to manage and deploy configurations, including feature flags, allowing controlled rollouts.

2. Key AWS AppConfig Features:

Feature Flags: Enable or disable features dynamically without redeploying code.

Variants: Define different configurations for subsets of users.

Targeting Rules: Specify rules for which users receive a particular variant.

3. Explanation of the Options:

Option A:"Set up a custom script within the application to randomly select 15% of users. Assign a flag for the new feature to the selected users."While possible, this approach requires significant operational effort to manage user selection and ensure randomness. It does not leverage AWS AppConfig's built-in capabilities, which increases overhead.

Option B:"Create separate AWS AppConfig feature flags for both groups of users. Configure the flags to target 15% of users."Creating multiple feature flags for different user groups complicates configuration management and does not optimize the use of AWS AppConfig.

Option C:"Create an AWS AppConfig feature flag. Define a variant for the new feature, and create a rule to target 15% of users."This is the correct solution. Using AWS AppConfig feature flags with variants and targeting rules is the most efficient approach. It minimizes operational overhead by leveraging AWS AppConfig's built-in targeting and rollout capabilities.

Option D:"Use AWS AppConfig to create a feature flag without variants. Implement a custom traffic splitting mechanism in the application code."This approach requires custom implementation within the application code, increasing complexity and operational effort.

4. Implementation Steps for Option C:

Set Up AWS AppConfig:

Open the AWS Systems Manager Console.

Navigate to AppConfig.

Create a Feature Flag:

Define a new configuration for the feature flag.

Add variants (e.g., "enabled" for the new feature and "disabled" for no change).

Define a Targeting Rule:

Use percentage-based targeting to define a rule that applies the "enabled" variant to 15% of users.

Targeting rules can use attributes like user IDs or geographic locations.

Deploy the Configuration:

Deploy the configuration using a controlled rollout to ensure gradual exposure.

Requirement Summary:

DynamoDB Provisioned Mode

ProvisionedThroughputExceededException error occurring

App hosted on a small burstable EC2 instance

Option A: Move to a larger EC2 instance

May improve local performance, but does not solve DynamoDB provisioned throughput limits.

Option B: Increase DynamoDB RCUs

Valid: This directly increases the read throughput capacity of the table.

Helps handle more traffic and reduce throughput exceptions.

Option C: Reduce frequency via exponential backoff

Best practice: Using exponential backoff and jitter reduces request pressure and spreads retries out to avoid spiking.

Option D: Increase frequency of retries by reducing delay

Opposite of best practice. Increases load, worsening the issue.

Option E: Change to on-demand mode

Also valid in general, but question is scoped around provisioned capacity.

Since minimizing cost or workload type isn't mentioned, and scaling the existing model is the focus, prefer B and C.

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

Exponential backoff: https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/Programming.Errors.html

Capacity modes: https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/HowItWorks.ReadWriteCapacityMode.html

Amazon CloudWatch is the service that collects and stores logs from AWS Lambda functions. The developer can use CloudWatch Logs Insights to query and analyze the logs for errors and metrics. Option A is not correct because Amazon S3 is a storage service that does not store Lambda function logs. Option B is not correct because AWS CloudTrail is a service that records API calls and events for AWS services, not Lambda function logs. Option D is not correct because Amazon DynamoDB is a database service that does not store Lambda function logs.

The correct answer is C. The developer did not update the Docker image tag to a new version.

C. The developer did not update the Docker image tag to a new version. This is correct. When deploying an application to Amazon EKS, the developer needs to specify the Docker image tag that contains the application code and configuration. If the developer does not update the Docker image tag to a new version after making changes to the application, the EKS cluster will continue to use the old Docker image tag that references the original backend resources. To fix this issue, the developer should update the Docker image tag to a new version and redeploy the application to the EKS cluster.

A. The developer did not successfully create the new AWS account. This is incorrect. The creation of a new AWS account is not related to the application’s connection to the backend resources. The developer can use any AWS account to host the EKS cluster and the backend resources, as long as they have the proper permissions and configurations.

B. The developer added a new tag to the Docker image. This is incorrect. Adding a new tag to the Docker image is not enough to deploy the changes to the application. The developer also needs to update the Docker image tag in the EKS cluster configuration, so that the EKS cluster can pull and run the new Docker image.

D. The developer pushed the changes to a new Docker image tag. This is incorrect. Pushing the changes to a new Docker image tag is not enough to deploy the changes to the application. The developer also needs to update the Docker image tag in the EKS cluster configuration, so that the EKS cluster can pull and run the new Docker image.

This solution meets the requirements because it uses a PostgreSQL-compatible database that can secure and regularly rotate database credentials without requiring additional programming overhead. Amazon Aurora PostgreSQL is a relational database service that is compatible with PostgreSQL and offers high performance, availability, and scalability. AWS Secrets Manager is a service that helps you protect secrets needed to access your applications, services, and IT resources. You can store database credentials in AWS Secrets Manager and use them to access your Aurora PostgreSQL database. You can also enable automatic rotation of your secrets according to a schedule or an event. AWS Secrets Manager handles the complexity of rotating secrets for you, such as generating new passwords and updating your database with the new credentials. Using Amazon DynamoDB for the database will not meet the requirements because it is a NoSQL database that is not compatible with PostgreSQL. Using AWS Systems Manager Parameter Store for storing and rotating database credentials will require additional programming overhead to integrate with your database.

Comprehensive and Detailed Step-by-Step Explanation:

Option D: Use Lambda with Container Images

AWS Lambda supports container images up to 10 GB in size, making it suitable for applications with large dependencies, such as a video codec library larger than 250 MB.

By creating separate container images for video compression and decompression, the application can efficiently isolate functionality while ensuring that each function includes the required dependencies.

The container images are stored in Amazon ECR and used to create the Lambda functions.

Why Other Options Are Incorrect:

Option A: A single Lambda function with all functionalities and dependencies in one zip file is not feasible due to the 250 MB deployment package size limit for zip files.

Option B: Including the library in two separate zip files still exceeds the size limit for Lambda zip deployment packages.

Option C: While using a Lambda layer can reduce redundancy, the combined size of the layer and the zip files would exceed the limit of 250 MB.

The company already has SSM Agent deployed on all servers, which enables centralized, scalable fleet operations without logging in to each instance. The most operationally efficient solution is to automate key distribution using Systems Manager Run Command, executing a script across the entire fleet (or a targeted subset) in one action.

Option B uses S3 as a simple distribution source and Run Command to perform the update at scale. The developer can store the required key material in a controlled S3 location and write a script that places the key in the correct filesystem path (with correct permissions/ownership), updates authorized_keys if needed, and restarts any services if required. Run Command can target instances by tags, resource groups, or instance IDs and provides execution logging and status.

Option A is not operationally efficient because it requires logging into each server manually.

Option C and D push the distribution burden to humans (“grant team members permissions to download”), which is error-prone, slow, and not scalable. Also, distributing private keys broadly to individuals increases risk.

This benefit occurs when initializing the AWS SDK outside of the Lambda handler function because it allows the SDK instance to be reused across multiple invocations of the same function. This can improve performance and reduce latency by avoiding unnecessary initialization overhead. If the SDK is initialized inside the handler function, it will create a new SDK instance for each invocation, which can increase memory usage and execution time.

The solution that will meet the requirements most cost-effectively is to store the smart meter readings in an Amazon DynamoDB table. Create a composite key by using the location ID and timestamp columns. Use the columns to filter on the customers’ data. This way, the company can leverage the scalability, performance, and low latency of DynamoDB to store and retrieve the smart meter readings. The company can also use the composite key to query the data by location ID and timestamp efficiently. The other options either involve more expensive or less scalable services, or do not provide low-latency access to the current usage.

AWS CloudFormation is a service that allows developers to model and provision AWS resources using templates. A CloudFormation template can define the load test resources, such as EC2 instances, load balancers, and Auto Scaling groups. A CloudFormation stack set is a collection of stacks that can be created and managed from a single template in multiple Regions and accounts. The AWS CLI create-stack-set command can be used to create a stack set from a template and specify the Regions where the stacks should be created. Reference: Working with AWS CloudFormation stack sets

The bottleneck in this architecture is typically the relational database (RDS MySQL), which has limited concurrent connections and write throughput compared with bursty API traffic. Because transaction volume spikes during the day and drops to near zero at night, the system needs a way to absorb bursts without over-provisioning the database (which would be costly and underutilized at night).

A cost-effective way to add elasticity is to introduce Amazon SQS as a buffer between API ingestion and database writes. With SQS, devices (or the API/Lambda) enqueue transactions quickly, and Lambda consumers process the queue at a controlled rate. The key is to prevent Lambda from overwhelming RDS with too many concurrent writes or connections. Setting reserved concurrency on the Lambda function limits the maximum number of concurrent executions, which effectively caps database connection pressure and smooths write load. This prevents overload events that cause errors/timeouts while still allowing the system to scale up to the reserved concurrency limit during peaks.

Option D captures this correct control mechanism: SQS for buffering + Lambda reserved concurrency matched to the DB’s connection capacity.

Option C is incorrect because “enhanced fanout” is a Kinesis Data Streams feature, not an SQS feature.

Options A and B focus on scaling Aurora read replicas, which helps read scaling, not write-heavy transaction ingestion. POS transaction processing is typically write-intensive; scaling read replicas does not solve the core elasticity problem. Also, migrating databases is higher effort and cost than adding a queue and tuning Lambda concurrency.

Therefore, adding SQS and setting Lambda reserved concurrency to protect RDS connections is the most cost-effective elasticity improvement.

AWS X-Ray SDK: The primary way to enable X-Ray tracing within applications. The SDK sends data about requests and subsegments to the X-Ray daemon for service map generation.

Instrumenting All Services: To visualize a complete microservice architecture on the service map, each relevant service must include the X-Ray SDK.

The most secure and AWS-recommended way for a Lambda function to access other AWS services is to use an IAM execution role. Lambda assumes this role at runtime and receives temporary credentials that are automatically rotated by AWS. The developer attaches the necessary permissions (the existing IAM policy) to that role, and then configures the Lambda function to use the role.

Option B follows least privilege and avoids long-term credentials. It also integrates with AWS security tooling: IAM Access Analyzer, CloudTrail, and policy boundaries can all be applied cleanly. Because the policy already exists, this requires minimal extra work: create/choose the execution role, attach the policy, and assign the role to the function.

Option A is not correct because IAM policies are attached to IAM identities (users, groups, roles) — not directly to Lambda functions as standalone entities. Lambda permissions are granted through the function’s execution role.

Option C is insecure because it uses long-term IAM user access keys embedded in environment variables. Even if encrypted, this expands the blast radius, complicates rotation, and contradicts AWS best practices for avoiding static credentials inside code/runtime.

Option D is extremely insecure and noncompliant. Root user access keys should not be used for applications and should generally not exist.

Therefore, create and attach the existing policy to a Lambda execution IAM role and assign that role to the function.

This solution will meet the requirements by creating a single S3 bucket policy that denies access to the S3 bucket unless the request comes from one of the specified VPC endpoints. The aws:SourceVpce condition key is used to match the ID of the VPC endpoint that is used to access the S3 bucket. The StringNotEquals condition operator is used to negate the condition, so that only requests from the listed VPC endpoints are allowed. Option A is not optimal because it will create multiple S3 bucket policies, which is not possible as only one bucket policy can be attached to an S3 bucket. Option B is not optimal because it will use the aws:SourceVpc condition key, which matches the ID of the VPC that is used to access the S3 bucket, not the VPC endpoint. Option C is not optimal because it will use the StringNotEquals condition operator with a single value, which will deny access to the S3 bucket from all VPC endpoints except one.

Amazon Cognito User Pools: A managed user directory service, simplifying user registration and login.

Social Identity Providers: Cognito supports integration with external providers (e.g., Google, Facebook), reducing development effort.

IAM Roles for Authorization: Cognito-managed IAM roles grant fine-grained access to AWS resources (like Lambda functions).

Operational Overhead: Cognito minimizes the need to manage user identities and credentials independently.

Requirement Summary:

NLP Lambda function with a large pre-trained model

Lambda layer became 8.7 GB → Exceeds AWS limits

Function returns RequestEntityTooLargeException

Need: High-performing, portable, low initialization time

Important AWS Limits:

Lambda Layers size limit (combined across all layers): 250 MB (unzipped)

Deployment package size (unzipped): 250 MB

Lambda container image support allows up to 10 GB image size

Evaluate Options:

A: Store model in S3 and load during execution

Leads to cold start latency every time

Model loading from S3 is slower and not suitable for real-time NLP

Not optimal for performance

B: Use EFS mounted to Lambda

⚠️ Valid for large models, but adds latency during cold start as model loads from EFS

Requires EFS setup, VPC, and has added network I/O overhead

Still slower than bundling in container image

C: Split into five Lambda layers

Still violates the total layer size limit of 250 MB (unzipped)

You cannot exceed that even with multiple layers

D: Use Docker container image

Allows bundling up to 10 GB of dependencies and models

High portability and performance

Avoids latency of downloading models at runtime

Ideal for scientific/NLP models

Lambda container image support: https://docs.aws.amazon.com/lambda/latest/dg/images-create.html

Lambda limits: https://docs.aws.amazon.com/lambda/latest/dg/gettingstarted-limits.html

Using large models with Lambda: https://aws.amazon.com/blogs/machine-learning/deploying-large-machine-learning-models-on-aws-lambda-with-container-images/

Sensitive environment variables in Lambda should be protected using encryption helpers integrated with AWS KMS. Lambda environment variables are encrypted at rest, and when using a customer managed KMS key, teams can control the key policy, access, and rotation for their own microservices—matching the policy requirement that each team has “full control” over key rotation. AWS managed keys do not provide the same level of customer control over lifecycle and rotation decisions.

Therefore, the correct approach is to create customer managed KMS keys (one per team/service boundary or per microservice, depending on governance) and configure each Lambda function to use the relevant CMK for encrypting its environment variables. The Lambda execution role must have permission to use the key for decryption at runtime, typically requiring kms:Decrypt (and sometimes kms:DescribeKey depending on tooling). This is captured by Option B.

Option A and D use AWS managed keys, which do not satisfy the requirement for each team to control rotation. Rotation control (and policy ownership) is a key differentiator of customer managed keys.

Option C is not correct because the Lambda execution role does not need kms:CreateGrant and kms:Encrypt for decrypting environment variables at runtime. Over-permissioning increases risk. The Lambda service handles encryption of environment variables; the function runtime principally needs the ability to decrypt.

The solution that will meet the requirements is to write data to Amazon ElastiCache. This way, the application can write session data to a fast, scalable, and reliable in-memory data store that can be served reliably across multiple requests. The other options either involve writing data to persistent storage, which is slower and more expensive than in-memory storage, or writing data to the root filesystem, which is not shared among multiple EC2 instances.

DynamoDB Accelerator (DAX) provides a managed caching layer specifically optimized for DynamoDB, reducing latency for repeated read requests.

Why Option A is Correct:

Purpose-Built: DAX is designed for DynamoDB, enabling sub-millisecond response times for frequently accessed items.

Operational Efficiency: No need for additional application-level caching logic.

Why Not Other Options:

Option B: Auto Scaling increases capacity but does not address repetitive reads.

Option C: API Gateway caching helps reduce request processing time but does not optimize DynamoDB reads.

Option D: ElastiCache is a general-purpose cache, adding unnecessary complexity for DynamoDB use cases.

DynamoDB Accelerator (DAX)

This solution will meet the requirements by using AWS SAM CLI tool, which is a command line tool that lets developers locally build, test, debug, and deploy serverless applications defined by AWS SAM templates. The developer can use sam local generate-event command to generate sample events for different event sources such as API Gateway or S3. The developer can create automated test scripts that use sam local invoke command to invoke Lambda functions locally in an environment that closely simulates Lambda environment. The developer can check the response from Lambda functions and document how to run the test scripts for other developers on the team. The developer can also update CI/CD pipeline to run these test scripts before deploying with AWS CDK. Option A is not optimal because it will use cdk local invoke command, which does not exist in AWS CDK CLI tool. Option B is not optimal because it will use a unit testing framework that reproduces Lambda execution environment, which may not be accurate or consistent with Lambda environment. Option D is not optimal because it will create a Docker container from Node.js base image to invoke Lambda functions, which may introduce additional overhead and complexity for creating and running Docker containers.

The Immutable deployment method is the best choice when a company requires minimal downtime and automatic rollback in case of a failure. Here's why:

In Immutable deployments, a new set of instances is launched with the updated version. These instances are tested and validated before they replace the old instances. This ensures zero downtime and immediate rollback if the deployment fails.

All at once (A) causes downtime because the update replaces all instances simultaneously.

Rolling deployments (B) update a few instances at a time, but if a failure occurs midway, downtime or partial unavailability can happen.

Snapshots (C) are not a deployment strategy in Elastic Beanstalk.

Amazon S3 Glacier Instant Retrieval is an archive storage class that delivers the lowest-cost storage for long-lived data that is rarely accessed and requires retrieval in milliseconds. With S3 Glacier Instant Retrieval, you can save up to 68% on storage costs compared to using the S3 Standard-Infrequent Access (S3 Standard-IA) storage class, when your data is accessed once per quarter. https://aws.amazon.com/s3/storage-classes/glacier/instant-retrieval/

Understanding Storage Requirements:

Files are large and infrequently accessed, but need to be available within minutes when requested in the first year.

Long-term (7-year) retention is required.

Cost-effectiveness is a top priority.

Why S3 Glacier Instant Retrieval:

Matches the retrieval requirements (access within minutes).

More cost-effective than S3 Standard for infrequently accessed data.

Simpler to use than traditional Glacier where retrievals take hours.

Why S3 Glacier Deep Archive:

Most cost-effective S3 storage class for long term archival.

Meets the 7-year retention requirement.

S3 Lifecycle Policy:

Automate the transition from Glacier Instant Retrieval to Glacier Deep Archive after one year.

Optimize costs by matching storage classes to access patterns.

Reserved concurrency guarantees a specific number of concurrent executions for a critical Lambda function. This ensures that the critical function always has sufficient resources to execute, even if other functions are consuming concurrency.

Why Option A:

Ensures Function Availability: Reserved concurrency isolates the critical Lambda function from other functions.

Low Overhead: Configuring reserved concurrency is straightforward and requires minimal setup.

Why Not Other Options:

Option B: Provisioned concurrency is ideal for reducing cold starts, not for managing execution limits.

Option C & D: Alarms and re-invocation mechanisms add complexity without resolving the root cause.

Managing Concurrency for AWS Lambda

AWS best practice is to store sensitive values like database passwords, OAuth tokens, and API keys in a dedicated secrets service rather than in source code or configuration files. AWS Secrets Manager is specifically designed for this use case: secure storage, retrieval through APIs/SDKs, fine-grained IAM access control, encryption at rest, auditing through CloudTrail, and—critically—built-in secret rotation.

AWS documentation describes Secrets Manager rotation as an automated process that uses a rotation schedule and typically invokes a Lambda function to update the secret in both Secrets Manager and the target system (for example, a database). The rotation schedule can be configured to a custom interval, including every 6 months, satisfying the requirement.

Option A is incorrect because AWS KMS manages encryption keys, not application secrets. KMS key rotation rotates KMS keys, not database credentials or API tokens.

Option B is incorrect because ACM manages TLS certificates, not arbitrary secrets like API keys and OAuth tokens.

Option C is incorrect because EventBridge can schedule events, but it is not a secrets storage service and does not provide secure secret lifecycle management and integrated rotation workflows by itself.

Therefore, storing secrets in AWS Secrets Manager and enabling automatic rotation with a 6-month schedule is the correct solution.

Amazon Elastic Block Store (Amazon EBS) provides block level storage volumes for use with Amazon EC2 instances1. Amazon EBS encryption offers a straight-forward encryption solution for your EBS resources associated with your EC2 instances1. When you create an encrypted EBS volume and attach it to a supported instance type, the following types of data are encrypted: Data at rest inside the volume, all data moving between the volume and the instance, all snapshots created from the volume, and all volumes created from those snapshots1. Therefore, option A is correct.

Secrets Management: AWS Secrets Manager is designed specifically for storing and managing sensitive credentials.

Environment Isolation: Creating separate secrets for each environment (development, staging, etc.) ensures clear separation and prevents accidental leaks.

Automatic Rotation: Secrets Manager provides built-in rotation capabilities, enhancing security posture.

Versioning: Tracking changes to secrets is essential for auditing and compliance.

The requirement is specific: certain JSON fields must be encrypted at the edge (before traversing the origin path) and must remain encrypted end-to-end through CloudFront → API Gateway → Lambda → DynamoDB. The AWS feature designed for encrypting specific fields at the edge is CloudFront field-level encryption. With field-level encryption, CloudFront encrypts designated fields in an HTTPS request before forwarding the request to the origin. The origin (API Gateway/Lambda) receives the encrypted values, and only trusted components that have the private key can decrypt them—ensuring the fields remain protected throughout the stack.

Option B matches this exactly: field-level encryption uses an asymmetric key pair (public key used by CloudFront to encrypt; private key held securely by the application for decryption). This satisfies “encrypted at the edge” and “remain encrypted throughout the full stack.”

The “PII encrypted at rest in DynamoDB” requirement is also satisfied because DynamoDB supports encryption at rest by default (and can use AWS owned or customer managed KMS keys). Field-level encryption adds granular protection for only the most sensitive attributes beyond standard at-rest encryption.

Option A (Lambda@Edge encryption) could encrypt at edge, but it is more custom code, higher operational complexity, and is not as direct/standard as CloudFront field-level encryption for encrypting request fields.

Option C encrypts in Lambda, not at the edge—data would already have traversed CloudFront and API Gateway in plaintext form before Lambda encrypts it, violating the “encrypted at the edge” requirement.

Option D is not a defined mechanism for edge encryption; API Gateway methods do not provide edge field encryption with KMS in this way.

Therefore, CloudFront field-level encryption is the correct solution.

IAM Roles for EC2: IAM roles are the recommended way to provide AWS credentials to applications running on EC2 instances. Here's how this works:

You create an IAM role with the necessary permissions to access the target S3 bucket.

You create an instance profile and associate the IAM role with this profile.

When launching the EC2 instance, you attach this instance profile.

Temporary Security Credentials: When the application on the EC2 instance needs to access S3, it doesn't directly use access keys. Instead, the AWS SDK running on the instance retrieves temporary security credentials associated with the role. These are rotated automatically by AWS.

AWS Systems Manager Parameter Store is a service that provides secure, hierarchical storage for configuration data management and secrets management. The developer can update the application to retrieve the variables from Parameter Store by using the AWS SDK or the AWS CLI. The developer can use unique paths in Parameter Store for each variable in each environment, such as /dev/api-url, /test/api-url, and /prod/api-url. The developer can also store the credentials in AWS Secrets Manager, which is integrated with Parameter Store and provides additional features such as automatic rotation and encryption.

The amount of memory you allocate to your Lambda function also determines how much CPU and network bandwidth it gets. Increasing the memory size can improve the performance of CPU-bound functions by giving them more CPU power. The CPU allocation is proportional to the memory allocation, so a function with 1 GB of memory has twice the CPU power of a function with 512 MB of memory. Reference: AWS Lambda execution environment

Requirement Summary:

WebSocket-based messaging app using API Gateway WebSocket APIs

Need to:

Identify clients repeatedly connecting/disconnecting

Be able to remove problematic clients

Evaluate Options:

A. Switch to HTTP APIs

HTTP APIs don’t support WebSocket connections

B. Switch to REST APIs

REST APIs are not compatible with WebSockets

C. Use the callback URL to disconnect clients

⚠️ Possible, but not a direct option

Callback URLs are used for sending messages to connected clients, not for disconnecting

D. Track client status in ElastiCache

Good solution: Store and update connection state (connected, disconnected, timestamps)

Helps track abuse or reconnections

E. Implement $connect and $disconnect routes

Required to capture connection lifecycle events

These can be used to log/store client behavior and decide on removal

WebSocket routes in API Gateway: https://docs.aws.amazon.com/apigateway/latest/developerguide/apigateway-websocket-api-route-selection.html

Managing WebSocket connections: https://docs.aws.amazon.com/apigateway/latest/developerguide/apigateway-websocket-api-mapping-template-reference.html

AWS CloudFormation is a service that enables developers to model and provision AWS resources using templates. The developer can add a CloudFormation Deletion Policy attribute with the Retain value to the database resource. This will prevent the database from being deleted when the stack is deleted or updated. The developer can also update the CloudFormation stack policy to prevent updates to the database. This will prevent accidental changes to the database configuration or properties.

The command that will meet the requirements is sam sync -watch. This command enables AWS SAM Accelerate mode, which allows the developer to only redeploy the Lambda functions that have changed. The -watch flag enables file watching, which automatically detects changes in the source code and triggers a redeployment. The other commands either do not enable AWS SAM Accelerate mode, or do not redeploy the Lambda functions automatically.

For API Gateway REST APIs, configuration changes (new resources/methods) do not automatically become callable from a stage until a new API Gateway Deployment is created and associated with the stage. In CloudFormation, an AWS::ApiGateway::Deployment resource is immutable and effectively represents a snapshot of the API configuration at a point in time. If the Deployment resource does not change (for example, its logical ID or a property that forces replacement), CloudFormation may not create a new deployment even though the stack update succeeds—resulting in the stage still pointing to the old deployment. The symptom is exactly what’s described: new methods return 404 because they are not part of the deployed snapshot.

A common operational fix is to create a new deployment explicitly during CI/CD. Option C does this by running aws apigateway create-deployment, which creates a fresh deployment that includes the new resources/methods and makes them available on the stage.

Option D is unrelated (CloudFront invalidation).

Options A and B do not affect API Gateway deployment snapshots.

???? In pure CloudFormation, another common pattern is to force a new deployment by changing the Deployment logical ID or embedding a timestamp/hash in the deployment description. But among the options given, running create-deployment is the direct fix.

Therefore, add a pipeline step to run aws apigateway create-deployment.

Step-by-Step Breakdown:

Requirement Summary:

Use AWS SAM to deploy:

1 Lambda Function

1 S3 Bucket

Lambda needs read-only access to the S3 bucket

Solution must be expressed via AWS SAM template

Option A: Reference a second Lambda authorizer function

Incorrect: Lambda authorizers are used in API Gateway for authentication, not for granting S3 permissions.

Option B: Add a custom S3 bucket policy to the Lambda function

Incorrect: Bucket policies control who can access the bucket, not what the Lambda function can do.

The permission must be granted to the Lambda’s IAM execution role.

Option C: Create an Amazon SQS topic for only S3 object reads

Incorrect: SQS cannot read objects from S3 and is not relevant to this scenario.

Option D: Add the S3ReadPolicy template to the Lambda function's execution role

Correct: AWS SAM provides managed policy templates like AmazonS3ReadOnlyAccess and shortcuts like S3ReadPolicy.

You can apply these to the Lambda’s execution role using the Policies: section in your SAM template.

Example SAM YAML:

yaml

CopyEdit

Resources:

MyFunction:

Type: AWS::Serverless::Function

Properties:

CodeUri: my-code/

Handler: app.handler

Runtime: python3.11

Policies:

- S3ReadPolicy:

BucketName: !Ref MyBucket

MyBucket:

Type: AWS::S3::Bucket

Properties:

BucketName: my-personal-bucket-name

SAM Policy Templates: https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/serverless-policy-templates.html

Example using S3ReadPolicy: https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/serverless-policy-templates.html#s3-readpolicy

This solution allows the developer to test the changes without affecting the production environment. Cloning an API creates a copy of the API definition that can be modified independently. The developer can then add request validation to the new API and test it using a testing tool. After verifying that the changes work as expected, the developer can apply the same changes to the existing API and deploy it to production.

AWS SAM provides built-in tooling to generate realistic event payloads that match the structure of events produced by AWS services (such as S3, SNS, SQS, API Gateway, EventBridge, and others). The command designed specifically for this purpose is sam local generate-event. It produces sample event JSON that mirrors the format AWS services deliver to Lambda, which meets the requirement that payload structures match real service events.

This approach also requires the least development effort because the developer does not need to manually craft and maintain JSON payload files. Instead, the developer can generate the appropriate event type with SAM, optionally tweak values (bucket name, object key, request parameters), and then run sam local invoke using the generated payload. This significantly reduces mistakes and saves time, especially when testing multiple event sources.

Why other options are less suitable:

A (shareable test events) is vague and typically still requires manual creation/maintenance.

B requires manually creating payloads, which is error-prone and higher effort.

C adds unnecessary operational overhead (S3 storage) and still relies on manual payload creation.

Therefore, using sam local generate-event is the quickest and lowest-effort way to generate accurate AWS-service-like test payloads for local Lambda testing.

The requirement is to search for partial matches of email_address but scoped to a specific customer_type. In DynamoDB, efficient partial matching is performed with a Query on a sort key using the begins_with() condition. However, Query requires that the partition key be specified exactly, so we need an index where customer_type can be used as the partition key, and email_address can be used as the sort key for prefix matching.

A Global Secondary Index (GSI) can be added to an existing table without recreating it, and it can use a different partition key and sort key from the base table. Option A correctly proposes a GSI with:

Partition key: customer_type

Sort key: email_address

Then the Lambda can run a Query on the GSI like: customer_type = :ct AND begins_with(email_address, :prefix) which returns emails that start with the typed prefix for that customer type.

Option B is wrong because it keeps email_address as the partition key. That would require an exact email value (not a prefix) to Query, and you cannot do begins_with on a partition key; begins_with is for sort keys.

Options C and D are invalid because Local Secondary Indexes (LSIs) must share the same partition key as the base table (here, email_address). You cannot create an LSI with customer_type or job_title as the partition key when the table partition key is email_address.

Therefore, adding a GSI (customer_type PK, email_address SK) and querying it with begins_with is the correct solution.

Step-by-Step Breakdown:

Requirement Summary:

In-transit encryption (when data is moving between client and S3)

Encryption at rest using AWS KMS keys (which are rotatable on-demand)

Option A: Write an S3 bucket policy to allow only encrypted connections over HTTPS by using permissions boundary

Incorrect: Permissions boundaries are for IAM policy control, not S3 bucket policies.

Also, encryption enforcement in-transit is not achieved through permissions boundaries.

Option B: Configure an S3 bucket policy to enable client-side encryption

Incorrect: AWS does not enforce client-side encryption via S3 bucket policies.

Client-side encryption must be handled entirely by the application.

Also, KMS is for server-side encryption, not client-side.

Option C: Configure the application to encrypt the objects by using an AWS KMS customer managed key before uploading the objects

Correct: This fulfills the requirement of encrypting objects at rest using a KMS CMK, which can be rotated.

You can specify the KMS key using the SSE-KMS option when putting an object.

Option D: Write an S3 bucket policy to allow only encrypted connections over HTTPS by using the aws:SecureTransport condition

Correct: This enforces in-transit encryption, which ensures that only HTTPS connections are allowed.

This is the AWS-recommended way to enforce encryption in transit via bucket policy.

Option E: Configure S3 Block Public Access settings for the S3 bucket to allow only encrypted connections over HTTPS

Incorrect: Block Public Access is used to prevent public exposure of the bucket.

It has nothing to do with encryption enforcement, whether in transit or at rest.

Using SSE-KMS: https://docs.aws.amazon.com/AmazonS3/latest/userguide/UsingKMSEncryption.html

aws:SecureTransport condition in bucket policies: https://docs.aws.amazon.com/AmazonS3/latest/userguide/amazon-s3-policy-keys.html

Server-side encryption with AWS KMS: https://docs.aws.amazon.com/AmazonS3/latest/userguide/serv-side-encryption.html

This solution will ensure that all orders and updates are stored to Amazon S3 with the least development effort because it uses DynamoDB Streams to capture changes in the DynamoDB table and trigger a Lambda function to write those changes to the S3 bucket. This way, the original Lambda function and API Gateway API endpoint do not need to be modified, and no additional services are required. Option A is not optimal because it will require more development effort to create a new Lambda function and a new API Gateway API endpoint, and to modify the original Lambda function to post updates to the new API endpoint. Option B is not optimal because it will introduce additional costs and complexity to use Amazon Kinesis Data Streams to create a new data stream, and to modify the Lambda function to publish orders to the data stream. Option D is not optimal because it will require more development effort to modify the Lambda function to publish to a new Amazon SNS topic, and to create and subscribe a new Lambda function to the topic.

https://docs.aws.amazon.com/lambda/latest/dg/nodejs-context.html https://docs.aws.amazon.com/lambda/latest/dg/nodejs-logging.html

There is no explicit information for the runtime, the code is written in Node.js.

AWS Lambda is a service that lets developers run code without provisioning or managing servers. The developer can use the AWS request ID field in the context object to obtain a unique identifier for each function invocation. The developer can configure the application to write logs to standard output, which will be captured by Amazon CloudWatch Logs. This solution will meet the requirement of logging key events with a unique identifier.

AWS Lambda layers are specifically designed to share common code and dependencies across multiple Lambda functions. A layer is a ZIP archive that contains libraries, a custom runtime, or other function dependencies. Layers are versioned, so the developer can publish a new layer version when libraries change and then update functions to reference the new version. This directly meets the requirements to centralize libraries, update conveniently, and keep libraries versioned—while requiring minimal development work.

Using a single shared layer significantly reduces duplication across function deployment packages. It also simplifies CI/CD because the developer can build and publish the layer independently and reuse it across many functions.

Option A (CodeArtifact) is useful for dependency management, but it still requires each Lambda deployment to package dependencies or download them during build. It’s more moving parts than a layer for this use case.

Option B (container images) can centralize dependencies but typically requires more effort: building, scanning, versioning container images, and updating function image URIs. It’s heavier operationally than layers for “shared custom libraries.”

Option D (EFS) can be mounted by Lambda, but introduces networking considerations (VPC config), EFS lifecycle/permissions, and is unnecessary for simply sharing libraries. Also, cold start and operational overhead can increase.

Therefore, creating a Lambda layer for the shared libraries is the least-effort, most standard approach.

Requirement Summary:

EKS containers send logs to CloudWatch Logs

Need to process logs in real time

Trigger logic based on a specific error in logs

Evaluate Options:

Option A: SNS topic with filter policy

SNS filter policies work on message attributes, not on CloudWatch Logs subscription filters

Option B: Subscription filter on log group

This enables real-time log processing

You can create a subscription filter with a pattern matching specific error strings

Sends matched logs to a Lambda function or Kinesis

Option C: CloudWatch agent operator for trace collection

Irrelevant for log processing

Used for monitoring and tracing, not real-time log filtering

Option D: Lambda function to process logs

Once logs match the pattern, Lambda can process and act (e.g., alert, store, analyze)

Option E: EventBridge rule on a schedule

Not real-time

Scheduled EventBridge rules are for cron-like tasks, not log stream processing

Subscription filters: https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/SubscriptionFilters.html

Real-time log processing with Lambda: https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/SubscriptionFilters.html#LambdaExample

Logs in EKS to CloudWatch: https://docs.aws.amazon.com/eks/latest/userguide/fargate-logging.html

This solution will meet the requirements by using DynamoDB Accelerator (DAX), which is a fully managed, highly available, in-memory cache for DynamoDB that delivers up to a 10 times performance improvement - from milliseconds to microseconds - even at millions of requests per second. The developer can use DAX to cache the trading data that is used to process each trading request, which will reduce the data retrieval time with the least possible effort. Option A is not optimal because it will add local secondary indexes (LSIs) for the trading data, which may not improve the performance or reduce the latency of data retrieval, as LSIs are stored on the same partition as the base table and share the same provisioned throughput. Option B is not optimal because it will store the trading data in Amazon S3 and use S3 Transfer Acceleration, which is a feature that enables fast, easy, and secure transfers of files over long distances between S3 buckets and clients, not between DynamoDB and clients. Option C is not optimal because it will add retries with exponential backoff for DynamoDB queries, which is a strategy to handle transient errors by retrying failed requests with increasing delays, not by reducing data retrieval time.

Why Option A is Correct:Converting a Lambda function to use a container image involves packaging the function code into a container image, storing the image in Amazon Elastic Container Registry (ECR), and updating the function to use the ECR repository URI.

Why Other Options are Incorrect:

Option B: SAM templates support container-based Lambda deployment, but storing the image in S3 is not applicable.

Option C: CloudFormation does not natively support specifying Lambda container images in S3.

Option D: While partially correct, it omits the need to specify the image tag for the deployment.

AWS Documentation References:

Lambda Container Images

DynamoDB TTL (Time-to-Live): A native feature that automatically deletes items after a specified expiration time.

Efficiency: Eliminates the need for scheduled deletion jobs, optimizing write throughput by avoiding potential throttling conflicts.

Seamless Integration: TTL works directly within DynamoDB, requiring minimal development overhead.

The customer must be able to download the generated video for at least 3 hours, which requires durable storage that persists beyond the Lambda execution environment. The best fit is Amazon S3 with a presigned URL.

Lambda’s /tmp storage (Option A) is ephemeral and scoped to the execution environment. It is not intended for durable customer downloads and can be deleted when the execution environment is recycled. A Lambda function URL also does not solve durable file hosting and would require the function to serve the content itself.

Option C uses S3 to store the video object durably and then issues a presigned URL that grants time-limited access to that specific object. Presigned URLs are designed for exactly this scenario: allow unauthenticated users temporary access to a private S3 object without making the bucket public. The developer can set the URL expiration to 3 hours (or longer), meeting the access requirement cleanly.

Option B is incorrect because S3 presigned URLs are for S3 objects, not EFS files. EFS does not natively use S3-style presigned URLs for external downloads.

Option D is not the right model: CloudFront can distribute S3 content and supports signed URLs/cookies, but CloudFront is a distribution layer, not an origin storage solution by itself. You would still need to store the video in S3 (or another origin). For the stated requirement, S3 + presigned URL is simpler and has less overhead.

Therefore, store videos in Amazon S3 and provide presigned URLs with a 3-hour expiration.

Why Option A is Correct:Auto-scaling with a target utilization ensures the DynamoDB table dynamically adjusts capacity based on workload, maintaining high performance while optimizing cost. Setting a reasonable target utilization minimizes overprovisioning and throttling risks.

Why Other Options are Incorrect:

Option B: On-demand capacity is costlier than provisioned throughput for predictable workloads.

Option C: Using manual CloudWatch alarms and Lambda for scaling is less efficient and adds operational overhead.

Option D: DAX accelerates read performance but does not improve write performance.

AWS Documentation References:

DynamoDB Auto Scaling

This solution allows the developer to create and delete branches in AWS CodeCommit by granting the codecommit:CreateBranch and codecommit:DeleteBranch permissions. These are the minimum permissions required for this task, following the principle of least privilege. Option B grants too many permissions, such as codecommit:Put*, which allows the developer to create, update, or delete any resource in CodeCommit. Option C grants too few permissions, such as codecommit:Update*, which does not allow the developer to create or delete branches. Option D grants all permissions, such as codecommit:*, which is not secure or recommended.

Amazon API Gateway supports mock integration responses, which are predefined responses that can be returned without sending requests to a backend service. Mock integration responses can be used for testing or prototyping purposes, or for simulating different backend responses based on certain conditions. A request mapping template can be used to select a mock integration response based on an expression that evaluates some aspects of the request, such as headers, query strings, or body content. This solution does not require any additional resources or code changes and has the least operational overhead. Reference: Set up mock integrations for an API Gateway REST API

https://docs.aws.amazon.com/apigateway/latest/developerguide/how-to-mock-integration.html

Amazon EventBridge: A service that reacts to events from various AWS sources, including S3. Rules define which events trigger actions (like invoking Lambda functions).

S3 Object Created Events: EventBridge can detect these, providing seamless integration for automated CSV processing.

S3 Lifecycle Rules: Allow for actions based on object age or prefixes. These can directly trigger Lambda functions for file processing.

The solution that will meet the requirements is to add a second cache behavior to the distribution with the same origin as the default cache behavior. Set the path pattern for the second cache behavior to the path of the login page, and make viewer access unrestricted. Keep the default cache behavior’s settings unchanged. This way, the login page can be accessed without authentication, while all other content remains secure and requires signed cookies. The other options either do not allow unauthenticated access to the login page, or expose private content to unauthorized users.

Requirement Summary:

Trigger an AWS Step Functions state machine (test execution)

Only when a specific AWS CloudFormation stack is deployed

Option A: Create a Lambda function to invoke the state machine

Valid approach: Lambda can be used as an intermediary trigger for Step Functions using the SDK (e.g., StartExecution API).

Offers flexibility (custom filtering, additional logic).

Option B: Create EventBridge rule filtering on UPDATE_IN_PROGRESS

Incorrect: UPDATE_IN_PROGRESS triggers before the stack is fully deployed.

You need to trigger after deployment, such as UPDATE_COMPLETE or CREATE_COMPLETE.

Option C: EventBridge Pipes with Lambda target filtering on UPDATE_IN_PROGRESS

Incorrect for same reason as B (wrong timing).

Also, EventBridge Pipes are not necessary here if you're using rules directly.

Option D: Pipe with EventBridge Rule as source and Step Functions as target

Invalid setup: EventBridge Pipes use event sources, not rules, as input.

This configuration is unsupported.

Option E: Add the state machine as a target of the EventBridge rule

Direct and low-overhead approach.

EventBridge natively supports Step Functions as a target.

You can trigger the state machine without a Lambda if the filter matches (e.g., ResourceStatus = CREATE_COMPLETE, with the correct StackId).

Step Functions as EventBridge target: https://docs.aws.amazon.com/eventbridge/latest/userguide/eventbridge-target-step-functions.html

EventBridge CloudFormation events: https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/using-cfn-listing-event-history.html

StartExecution API: https://docs.aws.amazon.com/step-functions/latest/apireference/API_StartExecution.html

The solution that will meet the requirements is to use multiple stages in API Gateway. Create a Lambda function for each environment. Configure API Gateway stage variables to route traffic to the Lambda function in different environments. This way, the company can test the code in a dedicated, monitored test environment before releasing it to the production environment. The company can also use stage variables to specify the Lambda function version or alias for each stage, and avoid hard-coding the Lambda function name in the API Gateway integration. The other options either involve using a single stage in API Gateway, which does not allow testing in different environments, or adding different code blocks for different environments in the Lambda function, which increases complexity and maintenance.

In CodePipeline, the service designed to run builds, execute unit/integration tests, and produce build artifacts (including test reports) is AWS CodeBuild. CodeBuild runs commands specified in a buildspec.yml file and supports reporting outputs such as test results (for example, JUnit XML), code coverage, and other build metadata.

Option A is correct because the developer can configure the CodeBuild project to run the test suite during the build phase and configure the buildspec to publish test report files. This integrates naturally into CodePipeline as a build stage, and the reports are generated consistently on each pipeline execution.

Option B is incorrect: CodeDeploy is for deploying applications (in-place/blue-green) and lifecycle hooks, not for standard build/test report generation.

Option C increases operational overhead by managing an EC2 build server, which is unnecessary when CodeBuild provides managed build environments.

Option D is unrelated: CodeArtifact is a package repository, not a test reporting solution.

Therefore, use CodeBuild and configure test reports via the buildspec.

Comprehensive and Detailed Step-by-Step Explanation:

Customer Managed Keys (CMK) for Granular Control (Option B):

Customer-managed KMS keys are required to meet the security policy requirement of team-specific control over KMS key rotation. Each team can manage the lifecycle of its own key.

The kms:Decrypt permission allows the Lambda function execution roles to decrypt the environment variables during runtime.

This solution adheres to the principle of least privilege and satisfies the need for team-specific key control.

Why Other Options Are Incorrect:

Option A: AWS-managed keys cannot provide team-specific control or support the custom rotation policy required by the teams.

Option C: Adding kms:CreateGrant and kms:Encrypt permissions to Lambda roles is unnecessary for this scenario. The key usage is limited to decryption at runtime.

Option D: AWS-managed keys still lack team-specific control, and adding kms:CreateGrant and kms:Encrypt is redundant.

This is a cross-account access pattern using STS AssumeRole. Account A owns the DynamoDB table and has created an IAM role (AccessPII) with permissions to access the table. Account A also configured a trust policy that allows principals from Account B to assume the role. That trust policy alone is not enough; the caller in Account B must also be allowed to call sts:AssumeRole.

Therefore, in Account B, the EC2 instance profile role (the role attached to the EC2 instances running the app) must have permission to assume the role in Account A. That is Option A.

Next, the application must actually obtain temporary credentials for the Account A role. The standard way is to call STS AssumeRole in the application logic (or use an SDK credential provider that assumes a role). This yields temporary credentials that have the permissions of the AccessPII role, allowing DynamoDB access in Account A. That is Option D.

Option B is not correct because granting direct DynamoDB table permissions in Account B does not grant access to a table owned by Account A unless Account A also grants access via a resource policy (DynamoDB resource policies exist but the scenario already sets up role assumption; the intended solution is AssumeRole).

Option C is incomplete/wrong: getting temporary credentials from the EC2 role alone gives permissions of the EC2 role in Account B, not the permissions in Account A. You must assume the cross-account role.

Option E is for IAM user MFA session tokens, not cross-account role assumption for an EC2 role.

Therefore, grant sts:AssumeRole to the EC2 role and call AssumeRole in code.

Why Option A is Correct:Creating a CloudWatch metric filter with "ERROR" as the search term ensures that occurrences of the word "ERROR" in logs are counted. An alarm can then be set to notify the SNS topic when the count exceeds 1. This is the standard approach to monitor specific patterns in CloudWatch Logs.

Why Other Options are Incorrect:

Option B: CloudWatch Logs Insights is used for querying logs manually and does not integrate directly with alarms for automated notifications.

Option C: SNS subscription filters are not a feature for filtering log patterns in CloudWatch Logs.

Option D: CloudWatch alarms do not directly include log filter patterns; a metric filter is needed to create the metric first.

AWS Documentation References:

Creating CloudWatch Metric Filters

Comprehensive Detailed Step by Step Explanation with All AWS Developer References:

In this scenario, the developer is configuring a mock integration in API Gateway. The integration request mapping template allows you to map the incoming request data to a format that the API expects. For mock integration, it's common to return specific HTTP status codes based on the conditions.

Using $context.integration.status: The $context.integration.status variable refers to the status of the API Gateway integration, which is useful for generating responses based on the condition. Option D correctly uses this variable to determine the HTTP status code, returning 200 for a successful mock request or 500 for a failure.

Alternatives:

Options A, B, and C do not use the correct context variables for handling mock integrations. These options would not return the correct status codes based on the actual integration status.

API Gateway Mapping Templates and Accessing Context Variables

This solution will meet the requirements with the least operational overhead because it uses Amazon S3 as a scalable, secure, and durable storage service for the reports. The presigned URL will allow customers to access their reports for a limited time (8 hours) without requiring additional authentication. The S3 Lifecycle configuration rules will automatically delete the reports that are older than 2 days, reducing storage costs and complying with the data retention policy. Option A is not optimal because it will incur additional costs and complexity to store the reports as DynamoDB items, which have a size limit of 400 KB. Option B is not optimal because it will not provide customers with access to their reports within one hour, as Amazon SNS email delivery is not guaranteed. Option D is not optimal because it will require more operational overhead to manage an RDS database and a Lambda function for storing and deleting the reports.

DynamoDB distributes throughput across partitions based on the hash key. A hot partition (caused by high usage of a specific hash key) can result in a ProvisionedThroughputExceededException, even if overall usage is below the provisioned capacity.

Why Option B is Correct:

Partition-Level Limits: Each partition has a limit of 3,000 read capacity units or 1,000 write capacity units per second.

Hot Partition: Excessive use of a single hash key can overwhelm its partition.

Why Not Other Options:

Option A: DynamoDB storage size does not affect throughput.

Option C: Provisioned scaling operations are unrelated to throughput errors.

Option D: Sort keys do not impact partition-level throughput.

DynamoDB Partition Key Design Best Practices

Amazon Simple Notification Service (Amazon SNS) is a fully managed messaging service that enables pub/sub communication between distributed systems. The developer can create an SNS topic and configure the Lambda function to publish messages with specific attributes to the topic. The developer can subscribe each partner to the SNS topic and apply the appropriate filter policy to the topic subscriptions. This way, each partner will receive updates for only their own orders based on the message attributes. This solution will meet the requirements in the most scalable way and allow adding new partners in the future with minimal code changes.

This solution allows the API to invoke the Lambda function asynchronously, which means that the API will return an immediate response without waiting for the function to complete. The X-Amz-Invocation-Type header specifies the invocation type of the Lambda function, and setting it to ‘Event’ means that the function will be invoked asynchronously. The function can then use Amazon Simple Email Service (SES) to send an email message when the report processing is complete.

The solution that will meet the requirements is to add a global secondary index (GSI) to the DynamoDB table with customer_type as the partition key and email_address as the sort key. Perform a query operation on the GSI by using the begins_with key condition expression with the email_address property. This way, the developer can search for partial matches of the email_address property of a particular customer type without recreating the DynamoDB table. The other options either involve using a local secondary index (LSI), which requires recreating the table, or using a different partition key, which does not allow filtering by customer_type.

AWS Secrets Manager: Built for managing secrets, providing encryption, automatic rotation, and access control.

Customer Master Key (CMK): Provides an extra layer of control over encryption through AWS KMS.

Automatic Rotation: Enhances security by regularly changing the secret.

User Data Script: Allows secrets retrieval at instance startup and sets them as environment variables for seamless use within the application.

Amazon ElastiCache for Memcached is a fully managed, multithreaded, and scalable in-memory key-value store that can be used to cache frequently accessed data and improve application performance1. By using Amazon ElastiCache for Memcached, the developer can reduce the load on the main database and handle high traffic surges more efficiently.

To use Amazon ElastiCache for Memcached, the developer needs to create a cache cluster with one or more nodes, and configure the application to store and retrieve data from the cache cluster2. The developer can use any of the supported Memcached clients to interact with the cache cluster3. The developer can also use Auto Discovery to dynamically discover and connect to all cache nodes in a cluster4.

Amazon ElastiCache for Memcached is compatible with the Memcached protocol, which means that the developer can use existing tools and libraries that work with Memcached1. Amazon ElastiCache for Memcached also supports data partitioning, which allows the developer to distribute data among multiple nodes and scale out the cache cluster as needed.

Using Amazon ElastiCache for Memcached is a simple and effective solution that meets the requirements with the least complexity. The developer does not need to change the database schema, migrate data to a different service, or use a different caching model. The developer can leverage the existing Memcached ecosystem and easily integrate it with the application.

Comprehensive and Detailed Explanation (AWS Verified – 250–300 Words)

When an Amazon S3 bucket is configured with default encryption using SSE-KMS, AWS Key Management Service (KMS) plays an active role in every PutObject request. Although the IAM user already has the s3:PutObject permission, this alone is not sufficient when SSE-KMS is enabled.

According to AWS documentation, when a client uploads an object to an S3 bucket encrypted with SSE-KMS, Amazon S3 must call AWS KMS on behalf of the caller to generate a unique data encryption key. This operation requires the kms:GenerateDataKey permission on the KMS key used for encryption. If this permission is missing, the PutObject request fails with an Access Denied error—even though S3 permissions appear correct.

AWS explicitly states that IAM principals uploading objects to SSE-KMS–encrypted buckets must be allowed to use the KMS key. At a minimum, the following KMS permissions are required:

kms:GenerateDataKey

(Implicitly) access allowed by the KMS key policy

Option C correctly resolves the issue by granting the IAM user permission to generate a data key via AWS KMS, enabling successful encryption during the upload process.

Option A is invalid because s3:EncryptionConfiguration is not required for object uploads.

Option B is unnecessary because the IAM user already has s3:PutObject.

Option D is incorrect because ACLs are not used to control KMS encryption permissions and are discouraged by AWS best practices.

The requirement is to test a new Lambda version without impacting the production API, and to do so with the least operational overhead. In API Gateway (REST API), the simplest pattern is to create a separate stage (for example, test) and use a stage variable to control which Lambda function version (or alias) the integration invokes.

With option B, the developer creates a new stage and defines a stage variable (commonly something like lambdaAlias or lambdaVersion). The API method integration URI can reference this stage variable so that each stage points to a different Lambda alias/version. The production stage (for example, prod) continues to call the stable version, while the test stage calls the new version. This cleanly isolates testing traffic and prevents any effect on the production REST API because stages have distinct invoke URLs and deployments.

This approach is operationally efficient because it avoids duplicating the API definition (new REST API) and avoids creating extra resources/methods that must be maintained. It also provides a repeatable process: future versions can be tested by updating only stage variables or alias routing rather than restructuring the API.

Why not the others:

A adds additional resources/methods and increases API surface area and maintenance.

C duplicates the entire API, increasing overhead and configuration drift risk.

D would impact production because it changes the integration used by the existing method.

Therefore, using a new stage + stage variables to route to the updated Lambda version meets the requirements with minimal overhead.

A function alias is a pointer to a specific Lambda function version. You can use aliases to create different environments for your function, such as development, testing, and production. You can also use aliases to perform blue/green deployments by shifting traffic between two versions of your function gradually. This way, you can easily roll back to a previous version if something goes wrong, without having to redeploy your code or change your configuration. Reference: AWS Lambda function aliases

This solution will meet the requirements by using AWS Config to monitor and evaluate whether Secrets Manager secrets are unused or have been deleted, based on specified time periods. The secrets manager-secret-unused managed rule is a predefined rule that checks whether Secrets Manager secrets have been rotated within a specified number of days or have been deleted within a specified number of days after last accessed date. The Amazon EventBridge rule will trigger a notification when the AWS Config managed rule is met, alerting the developer about unused secrets that can be removed without causing application downtime. Option A is not optimal because it will use AWS CloudTrail log file delivery to an Amazon S3 bucket, which will incur additional costs and complexity for storing and analyzing log files that may not contain relevant information about secret usage. Option C is not optimal because it will deactivate the application secrets and monitor the application error logs temporarily, which will cause application downtime and potential data loss. Option D is not optimal because it will use AWS X-Ray to trace secret usage, which will introduce additional overhead and latency for instrumenting and sampling requests that may not be related to secret usage.

Option B: The target group port in the ALB must match the port specified in the ECS task definition. If there is a mismatch, the ALB health check will fail since it cannot correctly route traffic to the container.

Option E: The ALB listener must have a default action that forwards requests to the correct target group associated with the ECS service. If this configuration is missing, the health check will fail as no traffic is routed to the service.

Option A is irrelevant to resolving health check issues since capacity providers relate to provisioning compute capacity.

Option C (hosted zone) is not directly related to ALB health checks.

Option D (redirecting traffic) is not related to ECS health check configurations.

The solution that will meet the requirements is to create an Amazon EventBridge rule that runs on a regular schedule to invoke the Lambda function. This way, the developer can use an automated and serverless way to invoke the function every 10 minutes. The developer can also use a cron expression or a rate expression to specify the schedule for the rule. The other options either involve using an Amazon EC2 instance, which is not serverless, or using environment variables or query parameters, which do not trigger the function.

In the CloudFormation template, the developer should create a parameter with the list of approved EC2 instance types as AllowedValues. This way, users can select the instance type they want to use when launching the CloudFormation stack, but only from the approved list.

S3Bucket and S3Key: These properties in a CloudFormation AWS::Lambda::Function resource specify the location of the function's code in S3.

Least Development Effort: This solution minimizes code changes, relying on CloudFormation to reference the existing S3 deployment package.

The requirement is application-level encryption (client-side) in addition to RDS encryption at rest, specifically to prevent database administrators from viewing PHI. Also, some PHI objects are large, so the solution must encrypt locally without size limitations.

AWS best practice for large data encryption with KMS is envelope encryption. With envelope encryption, the application generates a data encryption key (DEK) (typically obtained from KMS using GenerateDataKey). The application uses the plaintext DEK locally with a symmetric algorithm (such as AES-GCM) to encrypt the large PHI payload efficiently. The DEK itself is then protected by encrypting it with a KMS customer managed key (CMK), producing an encrypted data key that can be safely stored alongside the ciphertext. Only principals with permission to use the CMK can decrypt the DEK and then decrypt the PHI data. This ensures that even database administrators who can access the RDS data cannot read PHI without KMS authorization.

Option A is not suitable because the KMS Encrypt operation is intended for small payloads (KMS has size limits for direct encryption), making it inappropriate for large PHI files.

Option B is insecure because embedding encryption keys in source code violates key management best practices and makes rotation and compromise containment difficult.

Option C provides only storage-level encryption for the database volume; it does not ensure that PHI remains unreadable to administrators who can access the database content.

Therefore, using envelope encryption with a KMS customer managed key and storing the encrypted data key with the record is the most secure solution.

Requirement Summary:

E-commerce app using Amazon RDS for MySQL

Needs caching layer for most-viewed products

Evaluate Options:

A. Add a cache node to RDS

No such feature in RDS for MySQL

Caching must be implemented outside RDS

B. ElastiCache for Redis (OSS)

Purpose-built for caching frequently accessed data

Reduces read pressure on RDS

Fast in-memory access (microseconds)

Seamless integration into app logic

C. DynamoDB DAX

DAX is for accelerating DynamoDB, not RDS

D. RDS standby instance

Read from standby is not allowed

Standby is for failover only, not for load balancing

ElastiCache for Redis: https://docs.aws.amazon.com/AmazonElastiCache/latest/red-ug/WhatIs.html

Caching with Redis for RDS: https://aws.amazon.com/blogs/database/caching-strategies-using-amazon-elasticache-for-read-heavy-workloads-on-amazon-rds/

The requirement is specifically to have ECS automatically restore the last known good deployment when a new deployment fails, while the service is using the rolling update deployment type. Amazon ECS supports this through the deployment circuit breaker with the option to rollback on failures.

When enabled, the deployment circuit breaker monitors the deployment and detects failure conditions such as tasks repeatedly failing health checks, not reaching steady state, or being unable to start successfully. With rollback enabled, ECS automatically stops the failing deployment and reverts the service to the most recently completed (stable) task definition, restoring service stability without manual intervention.

Option D matches exactly: keep the rolling deployment type and enable circuit breaker + rollback. This is minimal change and directly addresses the behavior observed.

Options A and B are unrelated: service autoscaling adjusts desired count based on metrics and does not roll back failed deployments.

Option C could also achieve rollback behavior, but switching to blue/green introduces additional components (AWS CodeDeploy integration, target groups, and traffic shifting), which is unnecessary when the requirement can be met within rolling deployments.

Therefore, enabling the ECS deployment circuit breaker with rollback on failures for a rolling deployment is the correct solution.

Comprehensive and Detailed Step-by-Step Explanation:

To ensure successful software deployments using AWS CodeDeploy, the application specification file (appspec.yml or appspec.json) must adhere to specific requirements:

File Format Requirement (Option B):

The appspec.yml file is a YAML-formatted file required for defining deployment actions and file locations. This is the modern and recommended format for application specification files.

It can also be a JSON-formatted file named appspec.json, but YAML is most commonly used and accepted.

File Placement Requirement (Option D):

The application specification file must reside in the root directory of the application's source code. This is necessary so that AWS CodeDeploy can detect and use the file during deployment.

Incorrect Options:

Option A: While a JSON-formatted file (appspec.json) is valid, this is not a mandatory requirement. YAML is also acceptable, and this option does not account for it.

Option C: The application specification file is not required to be stored in AWS CodeBuild; it must be included in the source code's directory structure.

Option E: The application specification file does not need to be stored in Amazon S3. S3 is commonly used for application artifacts, but the appspec.yml or appspec.json file must exist within the deployment package or source code root.

Amazon S3 is a service that provides highly scalable, durable, and secure object storage. The developer can create an S3 bucket to host the repository and migrate the existing .xml files to the S3 bucket. The developer can update the application code to use the AWS SDK to read and write configuration files from S3. This solution will meet the requirement of high availability for the repository in a cost-effective way.

Requirement Summary:

Multi-tier app on EC2 + Aurora PostgreSQL

Must comply with least privilege and security policies

Need to manage credentials and API keys securely

Must support key rotation on demand

Evaluate Options:

A. Secrets Manager + AWS managed KMS keys

Best practice for secure secret storage

Supports auto rotation

Uses AWS SDK to fetch at runtime (secure, avoids hardcoding)

AWS managed keys are rotated automatically and easier to manage

B. Secrets Manager + customer managed keys

Also valid, but adds complexity

Since the question asks for LEAST development effort, AWS-managed keys are preferred

C. Store secrets in code

Violates all security best practices

D. Use SSM Parameter Store + AWS managed keys

Possible, but Secrets Manager is preferred when rotation is needed

Parameter Store does not natively rotate secrets

Secrets Manager: https://docs.aws.amazon.com/secretsmanager/latest/userguide/intro.html

Automatic key rotation: https://docs.aws.amazon.com/kms/latest/developerguide/rotate-keys.html

Best practices for secret management: https://docs.aws.amazon.com/secretsmanager/latest/userguide/best-practices.html

To monitor end-to-end requests and debug issues across microservices, you need distributed tracing. Among AWS tools, AWS X-Ray is specifically built for this use case.

Step-by-Step Breakdown:

Step 1: Understand the Requirement

The developer needs:

End-to-end request tracing across microservices

Debugging capability for performance issues or failures

This points directly to a distributed tracing solution rather than just logs or metrics.

Step 2: Evaluate the Options

Option A: Amazon CloudWatch

CloudWatch is powerful for metrics, logs, and alerts.

But it does not provide distributed tracing or request path visualization between microservices.

So, it's not sufficient alone for the requirement.

Option B: AWS CloudTrail

CloudTrail tracks API calls made through the AWS Management Console, CLI, SDKs.

It is meant for auditing and governance, not microservices request tracing.

Not suitable for debugging microservices interactions.

Option C: AWS X-Ray SDK with X-Ray service map

Purpose-built for distributed tracing

Automatically collects data about requests as they travel through your microservices

You can instrument your code with the AWS X-Ray SDK in Java, Node.js, Python, Go, .NET, etc.

Visualizes requests using a service map, showing latency, errors, and time spent in downstream services.

How it works:

Instrument each microservice using the AWS X-Ray SDK.

Use the SDK to trace requests, propagate trace headers across services.

The X-Ray daemon collects and sends trace data to the X-Ray service.

You can view the service map, see bottlenecks, error rates, etc.

Option D: AWS Health

AWS Health provides information on AWS service outages or account-level events.

It does not monitor your application or microservices health or request flows.

Correct Choice: C is the only option that meets the developer's requirement to monitor end-to-end request paths and debug issues in a microservices architecture.

AWS Developer References:

AWS X-Ray Documentation: https://docs.aws.amazon.com/xray/latest/devguide/aws-xray.html

Instrumenting your application: https://docs.aws.amazon.com/xray/latest/devguide/xray-sdk.html

Viewing the Service Map: https://docs.aws.amazon.com/xray/latest/devguide/xray-console-service-map.html

Tracing Microservices with AWS X-Ray: https://aws.amazon.com/blogs/compute/tracing-microservices-aws-x-ray/