Google Updated Professional-Cloud-DevOps-Engineer Exam Questions and Answers by junior

Comprehensive and Detailed Explanation From General GKE and GitOps Knowledge:

The requirements are:

Enforce constraint templates (implying a policy agent like OPA Gatekeeper) on GKE.

Store policy parameters in a GitHub repository.

Automatically apply changes from the GitHub repository to the clusters.

This is a classic GitOps scenario.

A. Set up a GitHub action to trigger Cloud Build when there is a parameter change. In Cloud Build, run a gcloud CLI command to apply the change.This is a plausible CI/CD approach. GitHub Actions can trigger Cloud Build, which can then use kubectl or gcloud to apply configurations. However, this is a push-based imperative approach. GitOps tools offer a more declarative, pull-based model specifically designed for syncing Kubernetes configurations.  

B. When there is a change in GitHub, use a webhook to send a request to Cloud Service Mesh, and apply the change.Cloud Service Mesh (based on Istio) is primarily for managing traffic, security, and observability for microservices. It's not designed for applying general Kubernetes policy configurations like Gatekeeper constraints from a Git repository.  

C. Configure Config Sync with the GitHub repository. When there is a change in the repository, use Config Sync to apply the change.Config Sync is a Google Cloud product specifically designed for GitOps with GKE (and other Kubernetes clusters). It synchronizes configurations (including CustomResourceDefinitions for constraint templates and the constraints themselves) from a Git repository (like GitHub) to your clusters. It continuously monitors the repository and automatically applies any committed changes to the clusters, ensuring they remain in the desired state. This perfectly matches the requirements.  

D. Configure Config Connector with the GitHub repository. When there is a change in the repository, use Config Connector to apply the change.Config Connector allows you to manage Google Cloud resources (like Pub/Sub topics, Spanner instances, etc.) using Kubernetes-style declarative configurations and kubectl. While it uses Kubernetes tooling, its primary purpose is managing Google Cloud resources, not syncing general Kubernetes configurations like Gatekeeper constraints from Git. Config Sync is the tool for syncing arbitrary Kubernetes manifests from Git to a cluster.  

Config Sync is the Google Cloud tool built for the exact purpose described: maintaining consistency between Kubernetes cluster configurations and a Git repository using a GitOps model.

Reference (Based on Google Cloud GKE and Config Sync documentation):

Config Sync Overview: https://cloud.google.com/anthos-config-management/docs/config-sync-overview or https://cloud.google.com/kubernetes-engine/docs/add-on/config-sync/overview (if referring to it as a GKE add-on). "Config Sync is a GitOps tool that helps you keep your Google Kubernetes Engine (GKE) Enterprise edition clusters synchronized with configs stored in a Git repository."  

It supports syncing various Kubernetes objects, including CustomResources, which are used by OPA Gatekeeper for defining constraints and constraint templates.

It automatically pulls changes from the Git repository and applies them, which meets the "automatically applied when changes occur" requirement.

"After making a change in the Cloud Build YAML configuration, you notice that no new artifacts are being built by the pipeline"- means something wrong on the recent change not with the image registry.

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.

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