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Google Professional-Cloud-DevOps-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Reliability and Site Reliability Engineering (SRE)	24%	- SLOs, SLIs, and SLAs 1. Error budget policies and management 2. Creating and interpreting Service Level Indicators 3. Defining and implementing Service Level Objectives 4. Setting appropriate Service Level Agreements - Incident management 1. Configuring automated incident response 2. Implementing on-call procedures 3. Post-incident reviews and blameless postmortems - Monitoring and observability 1. Implementing distributed tracing with Cloud Trace 2. Configuring Cloud Monitoring and Logging 3. Creating dashboards for service health visibility 4. Setting up alerting policies and incident management
Topic 2: Monitoring, Logging, and Debugging	12%	- Debugging and troubleshooting 1. Analyzing performance profiles with Cloud Profiler 2. Using Cloud Debugger and Error Reporting 3. Debugging with Cloud Trace distributed tracing - Cloud Operations Suite 1. Implementing Cloud Logging with log sinks 2. Configuring Cloud Monitoring metrics 3. Creating uptime checks and alerts - Log management 1. Structuring logs for efficient querying 2. Implementing log-based metrics 3. Configuring log retention policies
Topic 3: CI/CD Pipeline Development	26%	- CI/CD best practices 1. Configuring quality gates and code coverage 2. Managing secrets in CI/CD pipelines 3. Implementing shift-left testing - Containerization and Docker 1. Implementing container security best practices 2. Managing container registries 3. Creating optimized Docker images (multi-stage builds) - Designing and implementing CI/CD pipelines 1. Configuring build caching strategies 2. Setting up artifact management with Artifact Registry 3. Configuring build triggers and webhooks 4. Implementing build automation scripts 5. Designing pipeline architecture (Cloud Build, Jenkins, GitLab CI, etc.)
Topic 4: Microservices Architecture	14%	- API management 1. Exposing APIs with Cloud Endpoints / Apigee 2. Implementing API versioning strategies - Container orchestration with GKE 1. Managing pod lifecycle and resource quotas 2. Designing Kubernetes cluster architectures 3. Configuring node pools and auto-scaling 4. Implementing workload deployment and scaling - Service mesh and networking 1. Implementing service-to-service authentication 2. Implementing Anthos Service Mesh / Istio 3. Configuring traffic management and load balancing
Topic 5: Cloud Infrastructure Automation	24%	- Infrastructure as Code (IaC) 1. Implementing immutable infrastructure patterns 2. Managing infrastructure modules and state 3. Implementing with Terraform - Configuration management 1. Implementing configuration drift detection 2. Using Ansible for configuration management 3. Managing secrets with Secret Manager - Deployment strategies 1. Canary releases and progressive rollouts 2. Blue-green deployments 3. Rolling updates with Kubernetes 4. Feature flags with Firebase Remote Config or LaunchDarkly

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q125-Q130):

NEW QUESTION # 125
Your organization is running multiple Google Kubernetes Engine (GKE) clusters in a project. You need to design a highly-available solution to collect and query both domain-specific workload metrics and GKE default metrics across all clusters, while minimizing operational overhead. What should you do?

A. Use Prometheus Operator to install Prometheus in every cluster and scrape the metrics. Ensure that a Thanos sidecar is enabled on every Prometheus instance. Configure Thanos in the central cluster. Query the central Thanos instance.
B. Use Prometheus Operator to install Prometheus in every cluster and scrape the metrics. Configure remote-write to one central Prometheus. Query the central Prometheus instance.
C. Enable managed collection on every GKE cluster. Query the metrics in BigQuery.
D. Enable managed collection on every GKE cluster. Query the metrics in Cloud Monitoring.

Answer: D

Explanation:
Google recommends using managed collection for GKE monitoring to reduce operational overhead. This sends Kubernetes metrics and custom workload metrics directly to Cloud Monitoring.
"You can enable Managed Service for Prometheus, which provides a fully managed Prometheus-compatible monitoring solution integrated with Cloud Monitoring."
- Managed Collection in GKE
"It automatically collects and stores metrics in Cloud Monitoring with no need to manage a Prometheus server."
- Google Cloud Managed Prometheus
It's scalable, cost-effective, and requires no additional infrastructure like Thanos or Prometheus federation.

NEW QUESTION # 126
Some of your production services are running in Google Kubernetes Engine (GKE) in the eu-west-1 region. Your build system runs in the us-west-1 region. You want to push the container images from your build system to a scalable registry to maximize the bandwidth for transferring the images to the cluster. What should you do?

A. Push the images to Google Container Registry (GCR) using the eu.gcr.io hostname.
B. Push the images to Google Container Registry (GCR) using the gcr.io hostname.
C. Push the images to Google Container Registry (GCR) using the us.gcr.io hostname.
D. Push the images to a private image registry running on a Compute Engine instance in the eu-west-1 region.

Answer: A

NEW QUESTION # 127
Your application services run in Google Kubernetes Engine (GKE). You want to make sure that only images from your centrally-managed Google Container Registry (GCR) image registry in the altostrat-images project can be deployed to the cluster while minimizing development time. What should you do?

A. Create a custom builder for Cloud Build that will only push images to gcr.io/altostrat-images.
B. Add logic to the deployment pipeline to check that all manifests contain only images from gcr.io/altostrat-images.
C. Use a Binary Authorization policy that includes the whitelist name pattern gcr.io/attostrat-images/.
D. Add a tag to each image in gcr.io/altostrat-images and check that this tag is present when the image is deployed.

Answer: A

NEW QUESTION # 128
You created a Stackdriver chart for CPU utilization in a dashboard within your workspace project. You want to share the chart with your Site Reliability Engineering (SRE) team only. You want to ensure you follow the principle of least privilege. What should you do?

A. Share the workspace Project ID with the SRE team. Assign the SRE team the Monitoring Viewer IAM role in the workspace project.
B. Share the workspace Project ID with the SRE team. Assign the SRE team the Dashboard Viewer IAM role in the workspace project.
C. Click "Share chart by URL" and provide the URL to the SRE team. Assign the SRE team the Monitoring Viewer IAM role in the workspace project.
D. Click "Share chart by URL" and provide the URL to the SRE team. Assign the SRE team the Dashboard Viewer IAM role in the workspace project.

Answer: A

NEW QUESTION # 129
You are the on-call Site Reliability Engineer for a microservice that is deployed to a Google Kubernetes Engine (GKE) Autopilot cluster. Your company runs an online store that publishes order messages to Pub/Sub and a microservice receives these messages and updates stock information in the warehousing system. A sales event caused an increase in orders, and the stock information is not being updated quickly enough. This is causing a large number of orders to be accepted for products that are out of stock You check the metrics for the microservice and compare them to typical levels.

You need to ensure that the warehouse system accurately reflects product inventory at the time orders are placed and minimize the impact on customers What should you do?

A. Increase the Pod CPU and memory limits
B. Increase the number of Pod replicas
C. Add a virtual queue to the online store that allows typical traffic levels
D. Decrease the acknowledgment deadline on the subscription

Answer: B

Explanation:
Explanation
The best option for ensuring that the warehouse system accurately reflects product inventory at the time orders are placed and minimizing the impact on customers is to increase the number of Pod replicas. Increasing the number of Pod replicas will increase the scalability and availability of your microservice, which will allow it to handle more Pub/Sub messages and update stock information faster. This way, you can reduce the backlog of undelivered messages and oldest unacknowledged message age, which are causing delays in updating product inventory. You can use Horizontal Pod Autoscaler or Cloud Monitoring metrics-based autoscaling to automatically adjust the number of Pod replicas based on load or custom metrics.

NEW QUESTION # 130
......

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