Free Practice Questions for Google Professional-Cloud-DevOps-Engineer Exam

The correct answer is B. Create an attestation for the builds that pass the load test by using a private key stored in Cloud Key Management Service (Cloud KMS) authenticated through Workload Identity.

According to the Google Cloud documentation, Binary Authorization is a deploy-time security control that ensures only trusted container images are deployed on Google Kubernetes Engine (GKE) or Cloud Run1.Binary Authorization uses attestations to certify that a specific image has completed a previous stage in the CI/CD pipeline, such as passing a load test2.Attestations are signed by private keys that are associated with attestors, which are entities that verify the attestations3.To follow Google-recommended practices, you should store your private keys in Cloud Key ManagementService (Cloud KMS), which is a secure and scalable service for managing cryptographic keys4.You should also use Workload Identity, which is a feature that allows Kubernetes service accounts to act as Google service accounts, to authenticate to Cloud KMS and sign attestations without having to manage or expose service account keys5.

The other options are incorrect because they do not follow Google-recommended practices. Option A and option D require human intervention to sign the attestations, which is not scalable or automated. Option C exposes the service account JSON key as a Kubernetes Secret, which is less secure than using Workload Identity.

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."

https://sre.google/workbook/implementing-slos/

https://cloud.google.com/architecture/adopting-slos/

Latency is commonly measured as a distribution. Given a distribution, you can measure various percentiles. For example, you might measure the number of requests that are slower than the historical 99th percentile.

Comprehensive and Detailed 150 to 200 words of Explanation From Google Cloud DevOps guides documents:

Google Cloud Deploy is the platform's premier, fully-managed continuous delivery service specifically designed to simplify the progression of software through a series of environments (targets). For a Cloud Run application, Cloud Deploy provides a native, low-overhead way to define a single delivery pipeline that encapsulates both the staging and production targets. This approach directly satisfies the requirement for a fully-managed service while minimizing administrative toil compared to custom-scripted Cloud Build workflows (Option C) or complex GitOps controllers on GKE (Option B).

Key features of Cloud Deploy relevant here are its built-in support for mandatory manual approvals and automated canary/phased rollouts. By defining these in the delivery-pipeline YAML, you can ensure that a release is only promoted to production after a designated user approves it in the console or via CLI. Once approved, Cloud Deploy manages the traffic shifting to the new Cloud Run revision incrementally according to your specified percentages. This eliminates the need for managing multiple pipelines (Option D) and leverages native platform capabilities to ensure a reliable, repeatable, and secure deployment process that adheres to Google Cloud's modern DevOps and SRE deployment standards.

 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.

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'

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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.

https://cloud.google.com/monitoring/access-control