Professional-Cloud-DevOps-Engineer Sample Questions Answers

The correct answer is A. Check the node/ephemeral_storage/used_bytes metric by using Metrics Explorer.

The node/ephemeral_storage/used_bytes metric reports the total amount of ephemeral storage used by Pods on each node1.You can use Metrics Explorer to query and visualize this metric and filter it by node name, namespace, or Pod name2. This way, you can identify which Pods are consuming the most ephemeral storage and causing disk pressure on the nodes. You do not need to have execute access to the workloads or nodes to use Metrics Explorer.

The other options are incorrect because they require execute access to the workloads or nodes, which you do not have.The df -h and du -sh * commands are Linux commands that can measure disk usage, but you need to run them inside the Pods or on the nodes, which is not possible in your scenario34.

Cloud Build is a service that executes your builds on Google Cloud Platform infrastructure. Cloud Build can import source code from Google Cloud Storage, Cloud Source Repositories, GitHub, or Bitbucket, execute a build to your specifications, and produce artifacts such as Docker containers or Java archives1. Cloud Build can also run integration tests as part of your build steps2.

You can use Cloud Build to run tests in a specific folder by specifying the path to the folder in the dir field of your build step3. For example, if you have a folder named tests that contains your integration tests, you can use the following build step to run them:

steps:

- name: 'gcr.io/cloud-builders/go'

args: ['test', '-v']

dir: 'tests'

Copy

You can use Cloud Build to trigger builds for every GitHub pull request by using the Cloud Build GitHub app. The app allows you to automatically build on Git pushes and pull requests and view your build results on GitHub and Google Cloud console4. You can configure the app to run builds on specific branches, tags, or paths5. For example, if you want to run builds on pull requests that target the master branch, you can use the following trigger configuration:

includedFiles:

- '**'

name: 'pull-request-trigger'

github:

name: 'my-repo'

owner: 'my-org'

pullRequest:

branch: '^master$'

Using Cloud Build to run tests in a specific folder and trigger builds for every GitHub pull request is a good way to continuously test your feature branch and ensure that all code is tested before changes areaccepted. This way, you can catch any errors or bugs early and prevent them from affecting the main branch.

Using a Jenkins server for CI/CD pipelines is not a bad option, but it would require more setup and maintenance than using Cloud Build, which is fully managed by Google Cloud. Periodically running all tests in the feature branch is not as efficient as running tests for every pull request, as it may delay the feedback loop and increase the risk of conflicts or failures.

Using Cloud Build to run the tests after a pull request is merged is not a good practice, as it may introduce errors or bugs into the main branch that could have been prevented by testing before merging.

Asking the pull request reviewers to run the integration tests before approving the code is not a reliable way of ensuring code quality, as it depends on human intervention and may be prone to errors or oversights.

The best options for creating a CI/CD pipeline in Cloud Build to build an application container image and ensuring that production image builds are only run against the main branch and that the change control team approves all pushes to the main branch are to create a trigger on the Cloud Build job, set the repository event setting to Push to a branch, and configure a branch protection rule for the main branch on the repository. A trigger is a resource that starts a build when an event occurs, such as a code change. By creating a trigger on the Cloud Build job and setting the repository event setting to Push to a branch, you can ensure that the image build is only run when code is pushed to a specific branch, such as the main branch. A branch protection rule is a rule that enforces certain policies on a branch, such as requiring reviews, status checks, or approvals before merging code. By configuring a branch protection rule for the main branch on the repository, you can ensure that the change control team approves all pushes to the main branch.

The best option for implementing a scalable solution to support global Prometheus querying and minimize management overhead is to use Google Cloud Managed Service for Prometheus. Google Cloud Managed Service for Prometheus is a fully managed service that allows you to collect, query, and visualize metrics from your GKE clusters using Prometheus-based tooling. You can use Google Cloud Managed Service for Prometheus to query metrics across multiple clusters and regions using a global view. You can also use Google Cloud Managed Service for Prometheus to integrate with other Google Cloud services, such as Cloud Monitoring, Cloud Logging, and BigQuery. By using Google Cloud ManagedService for Prometheus, you can avoid managing and scaling your own Prometheus servers and focus on your application performance.

https://cloud.google.com/monitoring/support/notification-options#creating_channels

To configure SMS notifications, do the following:

In the SMS section, click Add new and follow the instructions. Click Save. When you set up your alerting policy, select the SMS notification type and choose a verified phone number from the list.

https://cloud.google.com/container-registry/docs/pushing-and-pulling

Eliminate bad monitoring : Unactionable alerts (i.e., spam)https://cloud.google.com/blog/products/management-tools/meeting-reliability-challenges-with-sre-principles

agree with kyubiblaze about having to remove unactionable items aka spam: "good monitoring alerts on actionable problems" @https://cloud.google.com/blog/products/management-tools/meeting-reliability-challenges-with-sre-principles

The correct answer is C. Change the machine type for your VMs to n2-standard-8.

According to the Google Cloud documentation, the network throughput performance of a Compute Engine VM depends on its machine type1. The n2-standard-2 machine type has a maximum egress bandwidth of 4 Gbps, which can be a bottleneck for serving large files. By changing the machine type to n2-standard-8, you can increase the maximum egress bandwidth to 16 Gbps, which can improve the network throughput performance and reduce the download time for users.You also need to enable per VM Tier_1 networking performance, which is a feature that allows VMs to achieve higher network performance than the default settings2.

The other options are incorrect because they do not improve the network throughput performance of your VMs.Option A is incorrect because Cloud NAT is a service that allows private IP addresses to access the internet, but it does not increase the network bandwidth or speed3.Option B is incorrect because adding additional network interfaces (NICs) or IP addresses per NIC does not increase ingress or egress bandwidth for a VM1.Option D is incorrect because deploying the Ops Agent can help you monitor and troubleshoot your VMs, but it does not affect the network throughput performance4.

The best option for managing all charts uniformly, with native access control and VPC Service Controls is to store public and private charts in OCI format by using Artifact Registry. Artifact Registry is a service that allows you to store and manage container images and other artifacts in Google Cloud. Artifact Registry supports OCI format, which is an open standard for storing container images and other artifacts such as Helm charts. You can use Artifact Registry to store public and private charts in OCI format and manage them uniformly. You can also use Artifact Registry’s native access control features, such as IAM policies and VPC Service Controls, to secure your charts and control who can access them.

Comprehensive and Detailed Explanation From General Google Cloud IAM and Organization Policy Knowledge:

The core requirement is to prevent accidental deletion of a Shared VPC host project, even by project owners, by ensuring that only users with a specific permission at the organization level can remove the lien that protects the project.

A lien (resourcemanager.projects.delete) has already been placed on the project. This prevents its deletion. The challenge is to prevent the removal of this lien by project-level administrators.

The permission to remove a lien is resourcemanager.projectLiens.update (or resourcemanager.projects.updateLiens as stated in the question, which implies a broader update capability including liens).

Option A (Enable VPC Service Controls for the container.googleapis.com API service): VPC Service Controls are for data exfiltration prevention by creating service perimeters. They do not directly control IAM permissions for lien management or project deletion.

Option B (Revoke the resourcemanager.projects.updateLiens permission from all users associated with the project): While this would prevent project-level users from removing the lien, it doesn't enforce therequirement that only users with this permission at the organization level can remove it. A project owner could potentially re-grant themselves this permission at the project level if not otherwise restricted. The goal is a stronger, centrally enforced restriction.

Option C (Enable the compute.restrictXpnProjectLienRemoval organization policy constraint): This is specifically designed for the scenario described.Organization Policies allow centralized control over resource configurations across the organization.

The compute.restrictXpnProjectLienRemoval constraint, when enforced (set to True), restricts the removal of liens on Shared VPC host projects. Only users who have the resourcemanager.projectLiens.update permission (or resourcemanager.projects.updateLiens) granted at the organization level can then remove such liens. This prevents project owners or other project-level principals from removing the lien unless they also have this specific permission at the org level.

Option D (Instruct teams to only perform IAM permission management as code with Terraform): While Infrastructure as Code (IaC) is a good practice for managing IAM, it's an operational guideline and doesn't technically enforce the restriction on lien removal. A user with sufficient project-level IAM permissions could still manually remove the lien via the console or gcloud if not prevented by an organization policy.

Therefore, enabling the compute.restrictXpnProjectLienRemoval organization policy is the direct and most effective way to meet the requirement.

Reference (Based on Google Cloud Organization Policy and Shared VPC documentation):

Google Cloud documentation on Resource Manager Liens: https://cloud.google.com/resource-manager/docs/project-liens

Google Cloud documentation on Organization Policy Constraints: https://cloud.google.com/resource-manager/docs/organization-policy/org-policy-constraints

Specifically, the compute.restrictXpnProjectLienRemoval constraint: "When set to true, liens on Shared VPC host projects can only be removed by users that have resourcemanager.projectLiens.update permission on the organization." (or similar wording indicating org-level permission is required). This constraint ensures that the protection afforded by the lien on a critical Shared VPC host project cannot be easily circumvented at the project level.

Comprehensive and Detailed Explanation:

Google Cloud Trace is specifically designed to analyze request latency and identify performance bottlenecks across services. Since the issue is related to slow performance after a new release, the best approach is to use Cloud Trace to:

Visualize request latency across services

Pinpoint slow dependencies affecting response time

Analyze performance trends over time

????Why not other options?

B (Error Reporting)❌→ Focuses on uncaught exceptions and crashes, not latency issues.

C (Cloud Profiler)❌→ Helps with CPU and memory analysis, not request tracing.

D (Prometheus for Cloud Monitoring)❌→ Useful for metrics collection, but not ideal for debugging specific request latency issues.

????Official Reference:

Google Cloud Trace Overview

The best option for automating scaling by only running enough Pods and nodes for the load is to configure the Horizontal Pod Autoscaler and enable the cluster autoscaler. The Horizontal Pod Autoscaler is a feature that automatically adjusts the number of Pods in a deployment or replica set based on observed CPU utilization or custom metrics. The cluster autoscaler is a feature that automatically adjusts the size of a node pool based on the demand for node capacity. By using both features together, you can ensure that your application runs enough Pods to handle the load, and that your cluster runs enough nodes to host the Pods. This way, you can optimize your resource utilization and cost efficiency.

Comprehensive and Detailed Explanation:

Cloud Deploy is a GCP-native managed CI/CD tool for reliable and automated releases across environments.

Artifact Registry Cleanup Policy automates image retention and deletion of old images, reducing storage costs and avoiding clutter.

Rollback and rollback policies should be handled by Cloud Deploy, not manual interventions.

????Why not other options?

A❌→ Cloud Build jobs for cleanup are not scalable or automated compared to Artifact Registry policies.

B❌→ Cloud Service Mesh is not related to deployment rollbacks or image cleanup.

D❌→ Cloud Deploy provides better rollback management than a custom rollback pipeline.

????Official Reference:

Google Cloud Deploy Overview

Artifact Registry Cleanup Policy

Google always aims to first stop the impact of an incident, and then find the root cause (unless the root cause just happens to be identified early on).

https://medium.com/velotio-perspectives/exploring-upgrade-strategies-for-stateful-sets-in-kubernetes-c02b8286f251

https://sre.google/workbook/incident-response/

"The main roles in incident response are the Incident Commander (IC), Communications Lead (CL), and Operations or Ops Lead (OL)."

The Operations Lead is responsible for managing the operational aspects of the incident, such as deploying fixes, rolling back changes, or restoring backups. The External Customer Communications Lead is not a standard role in incident response, but it could be delegated by the Communications Lead if needed.

The correct answer is A. Notify the team that their error budget is used up. Negotiate with the team for a launch freeze or tolerate a slightly worse user experience.

According to the Site Reliability Engineering (SRE) practices, an error budget is the amount of unreliability that a service can tolerate without harming user satisfaction1.An error budget is derived from the service-level objectives (SLOs), which are the measurable goals for the service quality2. When a service exceeds its error budget, it means that it has violated its SLOs and may have negatively impacted the users.In this case, the SRE team should notify the product team that their error budget is used up and negotiate with them for a launch freeze or a lower SLO3. A launch freeze means that no new features are deployed until the service reliability is restored. A lower SLO means that the product team accepts a slightly worse user experience in exchange for launching new features. Both options require a trade-off between reliability and innovation, and should be agreed upon by both teams.

The other options are incorrect because they do not follow the SRE practices.Option B is incorrect because it violates the principle of error budget autonomy, which means that each service should have its own error budget and SLOs, and should not borrow or reallocate them from other services4. Option C is incorrect because it does not address the root cause of the error budget overspend, and may create unrealistic expectations for the service reliability. Option D is incorrect because it does not prevent the possibility of introducing new errors or bugs with the feature launch, which may further degrade the service quality and user satisfaction.

 The best option for mitigating the incident impact on users and enabling the developers to troubleshoot the issue is to change the selector on the Service app-svc to app: my-app, version: blue. A Service is a resource that defines how to access a set of Pods. A selector is a field that specifies which Pods are selected by the Service. By changing the selector on the Service app-svc to app: my-app, version: blue, you can ensure that the Service only routes traffic to the Pods that have both labels app: my-app and version: blue. These Pods belong to the Deployment app-blue, which uses the previous version of the application. This way, you can mitigate the incident impact on users by switching back to the working version of the application. You can also enable the developers to troubleshoot the issue with the new version of the application in the Deployment app-green without affecting users.

The best option for sharing a Cloud Monitoring custom dashboard with a partner team is to provide the partner team with the dashboard URL to enable the partner team to create a copy of the dashboard. A Cloud Monitoring custom dashboard is a dashboard that allows you to create and customize charts and widgets to display metrics, logs, and traces from your Google Cloud resources and applications. You can share a custom dashboard with a partner team by providing them with the dashboard URL, which is a link that allows them to view the dashboard in their browser. The partner team can then create a copy of the dashboard in their own project by using the Copy Dashboard option. This way, they can access and modify the dashboard without affecting the original one.

https://cloud.google.com/blog/topics/retail/preparing-for-peak-holiday-season-while-wfh

 The best option for optimizing your cloud spend by ensuring that only a single instance of your infrastructure stack exists at a time is to confirm that the pipeline is storing and retrieving the terraform.tfstate file from Cloud Storage with the Terraform gcs backend. The terraform.tfstate file is a file that Terraform uses to store the current state of your infrastructure. The Terraform gcs backend is a backend type that allows you to store the terraform.tfstate file in a Cloud Storage bucket. By using the Terraform gcs backend, you can ensure that your pipeline has access to the latest state of your infrastructure and avoid creating multiple copies of the entire infrastructure stack.

https://www.terraform.io/docs/cloud/guides/recommended-practices/part3.3.html

https://cloud.google.com/build/docs/securing-builds/use-encrypted-credentials

The best option for giving developers the ability to test the latest revisions of the service before the service is exposed to customers is to run the gcloud run deploy booking-engine --no-traffic --tag devcommand and use the https://dev----booking-engine-abcdef.a.run.app URL for testing. The gcloud run deploy command is a command that deploys a new revision of your service or updates an existing service. By using the --no-traffic flag, you can prevent any traffic from being sent to the new revision. By using the --tag flag, you can assign a tag to the new revision, such as dev. This way, you can create a new revision of your service without affecting your customers. You can also use the tag-based URL (e.g., https://dev----booking-engine-abcdef.a.run.app) to access and test the new revision.

When a Service Level Objective (SLO) is violated in a canary deployment, the first recommended action is to roll back traffic to the last known good version.

"If your canary deployment results in errors or latency exceeding your SLOs, adjust the traffic splitting to route all traffic to a stable revision."

— Traffic Management in Cloud Run

This mitigates user impact immediately and aligns with SRE principles of error budget management and rapid remediation.

The best option for storing all organization logs for seven years and avoiding any future loss of log capture or stored logs due to misconfiguration or human error is to use Cloud Logging to configure an aggregated sink at the organization level to export all logs into Cloud Storage with a seven-year retention policy and Bucket Lock. Cloud Logging is a service that allows you to collect and manage logs from your Google Cloud resources and applications. An aggregated sink is a sink that collects logs from multiple sources, such as projects, folders, or organizations. You can use Cloud Logging to configure an aggregated sink at the organization level to export all logs into Cloud Storage, which is a service that allows you to store and access data in Google Cloud. A retention policy is a policy that specifies how long objects in a bucket are retained before they are deleted. Bucket Lock is a feature that allows you to lock a retention policy on a bucket and prevent it from being reduced or removed. You can use Cloud Storage with a seven-year retention policy and Bucket Lock to ensure that your logs are stored for seven years and protected from accidental or malicious deletion.

https://cloud.google.com/compute/docs/instances/preemptible

Comprehensive and Detailed Explanation From SRE Principles:

This scenario presents a classic SRE conflict: maintaining reliability (as dictated by the exhausted error budget and deployment freeze) versus delivering an urgent business requirement. The error budget policy is there for a reason – to protect users from further instability.

A. Start the deployment of the feature immediately: This directly violates the established error budget policy and the deployment freeze. While the feature is urgent, deploying without caution when the system is already unstable (as indicated by the exhausted error budget) is highly risky and could exacerbate existing problems or introduce new ones, further impacting revenue and customer trust.

B. Delay the deployment of the feature until the error budget is replenished: This strictly adheres to the policy but might not be acceptable given the "urgently required by your largest customer" clause. SRE principles allow for reasoned exceptions and risk management, not just blind adherence if the business context is compelling enough and risks are managed.

C. Re-run the unit tests, and start the deployment of the feature if the tests pass: Unit tests are foundational but insufficient to guarantee a complex application will perform reliably in production, especially when the system is already indicating instability (exhausted error budget). Passing unit tests doesn't negate the risk signaled by the depleted error budget.

D. Deploy the feature to a subset of users, and gradually roll out to all users if there are no errors reported: This is the most balanced SRE approach in this situation. It acknowledges the urgency while attempting to mitigate risk:Risk Mitigation: A canary release (deploying to a small subset of users) limits the potential negative impact if the new feature introduces new errors or worsens existing instability.

Observation: It allows for careful monitoring of the new release in the production environment with real users.

Data-Driven Decision: The decision to proceed with a wider rollout is based on observed behavior ("if there are no errors reported"), not just assumptions.

Controlled Rollout: A gradual rollout allows for quick rollback if issues arise.

While an exhausted error budget signals a deployment freeze, critical business needs can sometimes necessitate a carefully managed exception. A canary release is a standard SRE technique for deploying changes with reduced risk, making it the most appropriate course of action when faced with such conflicting priorities. The team would also need to communicate clearly about the risks and the rationale for this exception. It's implied that this urgent feature might also fix existing issues or is critical enough to warrant the carefully managed risk.

Reference (Based on SRE principles from Google's SRE books and general practices):

Error Budgets: "The SRE Book" (Site Reliability Engineering: How Google Runs Production Systems) discusses error budgets and deployment freezes. An exhausted error budget typically means no more risky changes until reliability improves.

Canary Releases: This is a fundamental practice for safely deploying new versions. It's about testing in production with a small percentage of traffic.

Managing Risk: SRE is about managing risk, not eliminating it entirely. In situations like this, a calculated risk with strong mitigation (canary, monitoring, rollback plan) can be justified for critical business needs. The decision involves weighing the risk of deploying against the risk of not deploying the urgent feature.

Option D represents a pragmatic SRE approach to navigate this difficult situation by minimizing the blast radius of the change.

The correct answer is D. Modify the application to use Cloud Logging software development kit (SDK), and send log entries with a jsonPayload field.

Cloud Logging SDKs are libraries that allow you to write structured logs from your Cloud Run application. You can use the SDKs to create log entries with a jsonPayload field, which contains a JSON object with the properties of your log entry.The jsonPayload field allows you to use advanced features of Cloud Logging, such as filtering, querying, and exporting logs based on the properties of your log entry1.

To use Cloud Logging SDKs, you need to install the SDK for your programming language, and then use the SDK methods to create and send log entries to Cloud Logging.For example, if you are using Node.js, you can use the following code to write a structured log entry with a jsonPayload field2:

// Imports the Google Cloud client library

const {Logging} = require('@google-cloud/logging');

// Creates a client

const logging = new Logging();

// Selects the log to write to

const log = logging.log('my-log');

// The data to write to the log

const text = 'Hello, world!';

const metadata = {

// Set the Cloud Run service name and revision as labels

labels: {

service_name: process.env.K_SERVICE || 'unknown',

revision_name: process.env.K_REVISION || 'unknown',

},

// Set the log entry payload type and value

jsonPayload: {

message: text,

timestamp: new Date(),

},

};

// Prepares a log entry

const entry = log.entry(metadata);

// Writes the log entry

await log.write(entry);

console.log(`Logged: ${text}`);

Using Cloud Logging SDKs is the best way to convert unstructured logs to structured logs, as it provides more flexibility and control over the format and content of your log entries.

Using a Fluent Bit sidecar container is not a good option, as it adds complexity and overhead to your Cloud Run application.Fluent Bit is a lightweight log processor and forwarder that can be used to collect and parse logs from various sources and send them to different destinations3. However, Cloud Run does not support sidecar containers, so you would need to run Fluent Bit as part of your main container image. This would require modifying your Dockerfile and configuring Fluent Bit to read logs from supported locations and parse them as JSON. This is more cumbersome and less reliable than using Cloud Logging SDKs.

Using the log agent in the Cloud Run container image is not possible, as the log agent is not supported on Cloud Run. The log agent is a service that runs on Compute Engine or Google Kubernetes Engine instances and collects logs from various applications and system components. However, Cloud Run does not allow you to install or run any agents on its underlying infrastructure, as it is a fully managed service that abstracts away the details of the underlying platform.

Storing the password directly in the code is not a good practice, as it exposes sensitive information and makes it hard to change or rotate the password. It also requires rebuilding and redeploying the application each time the password changes, which adds unnecessary work and downtime.

https://cloud.google.com/monitoring/agent/monitoring/troubleshooting#checklist

The best options for furthering your application’s reliability goal while balancing velocity, reliability, and business needs are to have more frequent or potentially risky application releases and to tighten the SLO to match the application’s observed reliability. Having more frequent or potentially risky application releases can help you increase the change velocity and deliver new features faster. However, this also increases the likelihood of consuming more error budget and reducing the reliability of your service. Therefore, you should monitor your error budget consumption and adjust your release policies accordingly. For example, you can freeze or slow down releases when the error budget is low, or accelerate releases when the error budget is high. Tightening the SLO to match the application’s observed reliability can help you align your service quality with your users’ expectations and business needs. However, this also means that you have less room for error and need to maintain a higher level of reliability. Therefore, you should ensure that your SLO is realistic and achievable, and that you have sufficient engineering resources and processes to meet it.

To exclude a subset of users or projects from Data Access audit logging, you use the exempted principals configuration. This allows you to selectively disable logs for specific groups, such as developers in the CustomerService folder.

"You can configure exempted principals so that audit logs are not generated for certain users, service accounts, or groups."

— Configuring Audit Logs

"Data Access logs are disabled by default because of their high volume, and you can enable them at the organization, folder, or project level."

— Audit Logs Overview

Thus, enabling audit logging org-wide and exempting a specific set of users (i.e., CustomerService folder) meets the need.

https://cloud.google.com/logging/docs/routing/overview

Comprehensive and Detailed Explanation From General SRE Principles and Google Cloud Knowledge:

Site Reliability Engineering (SRE) emphasizes reliability, automation, and a data-driven approach to operations. The goal is to minimize the "time to detect" (TTD) and "time to resolve" (TTR) for incidents.

Option A (Ensure that full autonomy and permissions are only granted to the on-call team): While the on-call team needs appropriate permissions to act decisively during an incident, granting full autonomy and only to them can be a bottleneck and goes against the principle of least privilege if not carefully scoped. Broader teams might need specific, controlled access for their responsibilities. SRE encourages empowering teams but within a structured framework.

Option B (Automate common tasks to analyze key impact information and intelligently suggest mitigating actions for the on-call team): This is a core SRE practice. Automation reduces toil, speeds up response, and ensures consistency. Analyzing impact and suggesting mitigations helps the on-call team resolve issues faster and more effectively.

Option C (Ensure that all teams can modify the production environment to resolve issues): This is generally a bad practice and against SRE principles of controlled changes and reducing the blast radius of errors. Production changes should be managed, audited, and ideally automated, not open to modification by all teams, as this increases the risk of unintended incidents.

Option D (Create an alerting mechanism for your SRE team based on your system's internal behavior): While alerting is crucial, SRE emphasizes alerting on symptoms that affect users (Service Level Objectives - SLOs) rather than just internal behavior or causes. Alerting solely on internal behavior can lead to alert fatigue and may not correlate directly with user impact. Good alerting focuses on user-facing impact first.

Option E (Create up-to-date playbooks with instructions for debugging and mitigating issues): Playbooks (or runbooks) are essential in SRE. They document known issues, troubleshooting steps, and mitigation procedures. Keeping them up-to-date ensures that on-call engineers can respond to incidents quickly and consistently, even for less common issues, thereby minimizing customer impact.

Therefore, automating incident response tasks (B) and maintaining clear, actionable playbooks (E) are two key SRE practices to implement for minimizing customer impact.

Reference (Based on SRE principles):

The SRE books by Google (e.g., "Site Reliability Engineering: How Google Runs Production Systems") heavily emphasize automation to reduce toil and the importance of playbooks for incident management.

Google Cloud SRE solutions: https://cloud.google.com/sre

Specifically, regarding playbooks and automation:"Playbooks should be living documents, updated regularly as systems change and new incidents provide new lessons."

"SREs aim to automate repetitive tasks (toil) to free up time for engineering projects that improve reliability."

The best option for detecting consumption of an error budget to protect customers and define release policies is to create a service level objective (SLO) and create an alert policy on select_slo_burn_rate. A SLO is a target value or range of values for a service level indicator (SLI) that measures some aspect of the service quality, such as availability or latency. An error budget is the amount of time or number of errors that a service can tolerate while still meeting its SLO. A select_slo_burn_rate is a metric that indicates how fast the error budget is being consumed by the service. By creating an alert policy on select_slo_burn_rate, you can trigger notifications or actions when the error budget consumption exceeds a certain threshold. This way, you can balance change, velocity, and reliability of the service by adjusting the release policies based on the error budget status.

The best option for providing your security team with the necessary permissions following the principle of least privilege and Google-recommended practices is to assign the roles/logging.privateLogViewer role to a group with all the security team members. The roles/logging.privateLogViewer role is a predefined role that grants read-only access to Data Access audit logs and other private logs in Cloud Logging. A group is a collection of users that can be assigned roles and permissions as a single unit. You can assign the roles/logging.privateLogViewer role to a group with all the security team members by using IAM policies. This way, you can provide your security team with the minimum level of access they need to view Data Access audit logs in the _Required bucket.

The best option for updating the instance template and minimizing disruption to the application and the number of pipeline runs is to set the create_before_destroy meta-argument to true in the lifecycle block on the instance template. The create_before_destroy meta-argument is a Terraform feature that specifies that a new resource should be created before destroying an existing one during an update. This way, you can avoid downtime and errors when updating a resource that is in use by another resource, such as an instance template that is used by a managed instance group. By setting the create_before_destroy meta-argument to true in the lifecycle block on the instance template, you can ensure that Terraform creates a new instance template with the updated machine type, updates the managed instance group to use the new instance template, and then deletes the old instance template.

The correct answer is D. Review the design of the product with the product development team to provide feedback early in the design phase.

According to the Google Cloud DevOps best practices, a Site Reliability Engineer (SRE) should collaborate with the product development team from the beginning of the product lifecycle, not just after the product is deployed or tested. This way, the SRE can provide operational input on the product design, such as scalability, reliability, security, and cost efficiency. The SRE can also help define service level objectives (SLOs) and service level indicators (SLIs) for the product, as well as monitoring and alerting strategies. By collaborating early and often, the SRE and the product development team can ensure that the product meets the operational requirements and expectations of the customers.

https://cloud.google.com/architecture/automated-canary-analysis-kubernetes-engine-spinnaker

https://spinnaker.io/guides/user/canary/best-practices/#compare-canary-against-baseline-not-against-production

https://cloud.google.com/logging/docs/access-control

The logging.configWriter role grants permissions to create, update, and delete log exports. This is the correct role to give team members who need to export logs2.

https://cloud.google.com/blog/products/gcp/available-or-not-that-is-the-question-cre-life-lessons

https://sre.google/sre-book/service-level-objectives/

Comprehensive and Detailed Explanation:

When using Managed Service for Prometheus, metrics may not appear in Cloud Monitoring if PodMonitoring is misconfigured. The most likely issue is that the PodMonitoring configuration does not reference a valid port.

PodMonitoring is required to collect metrics from workloads in GKE.

If the port is incorrect or missing, metrics won’t be scraped.

????Why not other options?

A (Check quota limits)❌→ If quotas were exceeded, you’d see explicit errors, but the question states no errors were encountered.

B (Grafana installation check)❌→ Grafana is not required for Prometheus metric collection.

C (monitoring.servicesViewer IAM role issue)❌→ This role allows viewing metrics, but does not affect metric collection.

????Official Reference:

Managed Service for Prometheus - PodMonitoring

GKE Monitoring Troubleshooting

a latency SLO is a service level objective that specifies a target level of responsiveness for a web application1. A latency SLO can be expressed as a percentile of latency over a time window, such as the 90th percentile of latency over 28 days2. A percentile of latency is the maximum amount of time that a given percentage of requests take to complete. For example, the 90th percentile of latency is the maximum amount of time that 90% of requests take to complete3.

To define a latency SLO, you need to consider the following factors24:

The expectations and satisfaction of your customers. You want to set a latency SLO that reflects the level of performance that your customers are happy with and willing to pay for.

The current and historical measurements of your latency. You want to set a latency SLO that is based on data and realistic for your web application.

The trade-offs and costs of improving your latency. You want to set a latency SLO that balances the benefits of faster response times with the costs of engineering work, infrastructure, and complexity.

Based on these factors, the best option for defining a latency SLO for your web application is option B. Option B sets the latency SLO to match the current measurement of your latency, which means that you are meeting the expectations and satisfaction of your customers. Option B also sets a realistic and achievable target for your web application, which means that you do not need to invest extra resources or effort to improve your latency. Option B also aligns with the best practice of setting conservative SLOs, which means that you have some buffer or margin for error in case your latency fluctuates or degrades5.

Google Workload Identity is the recommended method to allow GKE workloads to securely access Google Cloud APIs using short-lived credentials without managing keys.

"Workload Identity is the recommended way to access Google Cloud services from GKE. It replaces the older method of using service account keys."

— Workload Identity Overview

"You can configure a Kubernetes service account to impersonate a Google Cloud service account by granting the roles/iam.workloadIdentityUser role and using the iam.gke.io/gcp-service-account annotation."

— Configure Workload Identity

This satisfies the customer’s requirement for PoLP and avoids long-lived keys.