Pass the Google Cloud DevOps Engineer Professional-Cloud-DevOps-Engineer Questions and answers with CertsForce

https://cloud.google.com/build/docs/deploying-builds/deploy-gke

https://cloud.google.com/build/docs/securing-builds/configure-user-specified-service-accounts

The best option for deploying a new release of your microservice on Cloud Run and testing it extensively in the staging and production environments with minimal user and developer impact is to deploy the new version of the service to the staging environment with a new-release tag without serving traffic, test the new-release version, and if the test passes, gradually roll out this tagged version. A tag is a label that you can assign to a revision of your service on Cloud Run. You can use tags to create different versions of your service without affecting traffic. You can also use tags to gradually roll out traffic to a new version of your service by using traffic splitting. This way, you can test your new release extensively in both environments and minimize user and developer impact.

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 CI/CD Practices:

The issue is a runtime failure: the container fails to start due to a missing environment variable. This means the application expects an environment variable that wasn't provided when the container was run. The goal is to prevent this within the CI/CD workflow before it reaches deployment.

A. Run integration tests in the CI pipeline: Integration tests typically involve deploying the application (or a component of it) to a test environment and checking if its parts work together correctly. As part of this, the application would attempt to start up with its configured environment. An integration test suite could include a basic "smoke test" that simply verifies the application starts successfully. If a required environment variable is missing, the application would fail to start during this integration test phase in the CI pipeline, catching the error before a production deployment. Many integration test setups will try to mimic the target deployment environment including its configuration mechanisms (like environment variables).  

B. Implement static code analysis in the CI pipeline: Static code analysis tools check the code for potential bugs, style issues, and security vulnerabilities without actually running it. While useful, they are unlikely to catch a missing environment variable configuration, as this is an issue with the deployment configuration or runtime environment, not typically a static property of the code itself (unless the code hardcodes an expectation that could be flagged, but that's less direct).  

C. Use a canary deployment strategy: Canary deployments are a strategy for releasing software to production by first deploying to a small subset of users/servers. This helps limit the blast radius if an issue occurs in production. While a good practice for deployments, it doesn't prevent the issue from occurring in the first place; it just limits its impact once it does occur. The question asks to prevent recurrence within the CI/CD workflow (i.e., earlier).  

D. Enable Cloud Audit Logs for the deployment: Cloud Audit Logs record administrative actions and accesses within Google Cloud. While the deployment logs already indicated the failure, audit logs provide information about who did what and when regarding the deployment configuration or execution. They are useful for post-mortem analysis of the deployment process itself but don't directly prevent the application from failing due to a misconfiguration like a missing environment variable during the build and test stages.  

The most effective way to catch such an issue before a production deployment attempt is to have a test stage in the CI pipeline that attempts to run the application in an environment configured similarly to production, including expected environment variables. Integration tests (or even simpler smoke tests that check for successful startup) would achieve this.

Reference (Based on CI/CD best practices):

Continuous Integration (CI) principles emphasize automated testing at various levels (unit, integration, end-to-end) to catch issues early.  

A common CI pipeline stage is to build the application, then deploy it to a test/staging environment and run integration tests. If the application fails to start in this test environment due to a missing environment variable, the pipeline would fail, preventing a flawed release from proceeding further.

"Integration tests verify that different parts of your application work together correctly. This can include interactions with databases, external services, and ensuring the application starts and operates as expected with its runtime configuration."  

Catching configuration errors like missing environment variables is a key benefit of running integration or smoke tests in a CI environment that mirrors production.

https://cloud.google.com/anthos/clusters/docs/on-prem/1.7/concepts/logging-and-monitoring

The best option for minimizing the number of log sinks created and ensuring that the log sinks apply to future projects is to create an aggregated log sink in the Dev and Prod folders. An aggregated log sink is a log sink that collects logs from multiple sources, such as projects, folders, or organizations. By creating an aggregated log sink in each folder, you can export log entries for development projects to dev_dataset and log entries for production projects to prod_dataset. You can also use filters to specify which logs you want to export. Additionally, by creating an aggregated log sink at the folder level, you can ensure that the log sink applies to future projects that are created under that folder.

The best way to prevent severe incidents from happening in the future is to ensure that test cases that catch errors of this type are run successfully before new software releases. This is aligned with the Site Reliability Engineering principle of testing for reliability.

The best option for deploying a new version of your containerized application to production on GKE and ensuring that the application does not have performance problems after deployment is to deploy the application through a continuous delivery pipeline by using canary deployments, use Cloud Monitoring to look for performance issues, and ramp up traffic as supported by the metrics. A canary deployment is a deployment strategy that involves releasing a new version of an application to a subset of users or servers and monitoring its performance and reliability. This way, you can test the new version in the production environment with real traffic and load, and gradually increase the traffic as the metrics indicate. You can use Cloud Monitoring to collect and analyze metrics from your application and GKE cluster, such as latency, error rate, CPU utilization, and memory usage. You can also use Cloud Monitoring to set up alerts and dashboards to track the performance of your application.

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