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

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

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

To maintain a secure software supply chain, Google Cloud recommends enabling On-Demand Scanning and Continuous Analysis via the Artifact Analysis API. For Artifact Registry, you should enable automatic scanning, which scans images immediately upon upload. Crucially, your requirement asks for continuous scanning of existing images. Google Cloud’s Continuous Analysis does exactly this: it monitors vulnerability databases (like the CVE list) and automatically re-scans images stored in Artifact Registry whenever new threats are discovered. This ensures that an image that was "clean" last week is flagged immediately if a new zero-day vulnerability is identified today.

To address the requirement of tracking who pushed each image, Cloud Audit Logs is the standard tool. Every action in Artifact Registry, such as v1.repositories.images.create or v1.repositories.dockerImages.import, is recorded as an Administrative Write event. By inspecting these logs, security teams can identify the specific identity (service account or user email) associated with the push event. This combination of Continuous Analysis for threat detection and Cloud Audit Logs for traceability provides a robust security posture, minimizing administrative overhead compared to manual scripting or external storage solutions (Option D).

Comprehensive and Detailed Explanation:

For highly sensitive user data, you need to isolate applications while minimizing management overhead. The best practice is:

One GKE cluster per environment (Development and Production) → This provides a clear separation of concerns and avoids security risks from running different environments in the same cluster.

Each application in its own namespace → Namespaces provide logical isolation for different applications within the same cluster, reducing unauthorized access risks.

????Why not other options?

A (Single cluster for org with multiple namespaces for apps & envs)❌→ Bad security practice because mixing production and development in the same cluster increases the risk of privilege escalation.

C (Single cluster for org with namespaces per app)❌→ Still mixes development and production in the same cluster, violating isolation requirements for sensitive data.

D (One cluster per app)❌→ High operational overhead; unnecessary complexity for small to medium-scale deployments.

????Official Reference:

GKE Multi-Tenancy Best Practices

GKE Security Hardening

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.

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

Comprehensive and Detailed Explanation:

Cloud Run scales down to zero when idle. If CPU is allocated only during request processing, background telemetry tasks (like OpenTelemetry traces) may not complete before the container shuts down.

Solution: Configure CPU to be always-allocated → This ensures that background tasks (like trace exports) continue even when the service is idle.

????Why not other options?

A (Increasing CPU to 4 cores)❌→ More CPU won’t help if traces are being cut off due to shutdown.

B (Using an HTTP health check)❌→ Doesn’t affect OpenTelemetry traces.

C (CPU only during request processing)❌→ This causes the problem because traces need CPU after request completion to be exported.

????Official Reference:

Cloud Run Tracing with OpenTelemetry

Cloud Run Always-Allocated CPU

To restrict access to specific sensitive log fields (e.g. PII), Google Cloud offers Log field-level access control. You can apply field-level restrictions and then assign the Log Field Accessor role only to trusted personnel.

“You can use field-level access control to restrict access to certain fields in your logs. For example, you can restrict access to jsonPayload.user_email.”

— Cloud Logging Field-Level Access

“Grant the roles/logging.fieldAccessor role to principals that require access to these restricted fields.”

— Log Field Accessor Role

This lets your operations team continue using logs while ensuring that PII is only visible to the security team, fulfilling your compliance obligations.

To allow manual QA approval, you need separate pipeline stages (e.g., staging → production) and manual promotion between them.

"Cloud Deploy supports multi-stage pipelines where you can manually approve releases before promotion."

— Cloud Deploy Promotion

"Use a standard strategy when there are no automated tests or SLOs to support rollback decisions."

— Deployment Strategies

Canary is best used when automated health checks and metrics exist, which is not the case here.