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

The best option for storing all logs for one year and minimizing required code changes is to create a logs bucket and logging sink, set the retention on the logs bucket to 365 days, configure the logging sink to send logs to the bucket, and give your client access to the bucket to retrieve the logs. A logs bucket is a Cloud Storage bucket that is used to store logs from Cloud Logging. A logging sink is a resource that defines where log entries are sent, such as a logs bucket, BigQuery dataset, or Pub/Sub topic. You can create a logs bucket and logging sink in Cloud Logging and set the retention on the logs bucket to 365 days. This way, you can ensure that all logs are stored for one year and protected from deletion. You can also configure the logging sink to send logs from Cloud Run and Cloud Functions to the logs bucket without any code changes. You can then give your client access to the logs bucket by using IAM policies or signed URLs.

Rollback to previous stable version. Then you need to find what is causing the issue.

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.

To speed up Docker builds, optimize layer caching. Google Cloud recommends using --cache-from with Artifact Registry, and placing frequently changed files late in the Dockerfile so earlier layers can be reused.

“Put instructions that are less likely to change (like installing packages) early in the Dockerfile, and more frequently changing lines (like copying app code) near the end.”

— Dockerfile Best Practices

“Use the --cache-from argument to pull layers from previously built images in Artifact Registry.”

— Cloud Build Docker Caching

This dramatically reduces build time by avoiding redundant rebuilds of static layers.

OOMKilled means the containers exceeded their allocated memory. Since the HPA isn't scaling up (stuck at min), this indicates the issue is not scale-related but container memory limits are too low.

"Pods terminated with OOMKilled have exceeded their memory limits. You should adjust the container's resource requests and limits."

— Kubernetes OOMKill Debug Guide

Scaling the number of replicas won't help if each replica crashes. You need to increase memory limits so pods can run reliably before autoscaling can even work.

The easiest way to collect detailed metrics such as system resource utilization is to import the Stackdriver Profiler package, and configure it to relay function timing data to Stackdriver for further analysis. This way, you can use centralized GCP services without modifying your code or setting up additional tools.

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.