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

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

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Professional-Cloud-DevOps-Engineer Prüfungsübungen - Professional-Cloud-DevOps-Engineer Prüfungs

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Professional-Cloud-DevOps-Engineer Prüfungsfragen mit Lösungen (Q65-Q70):

65. Frage
You need to create a Cloud Monitoring SLO for a service that will be published soon. You want to verify that requests to the service will be addressed in fewer than 300 ms at least 90% Of the time per calendar month.
You need to identify the metric and evaluation method to use. What should you do?

A. Select a latency metric for a request-based method of evaluation.
B. Select an availability metric for a request-based method of evaluation.
C. Select a latency metric for a window-based method of evaluation.
D. Select an availability metric for a window-based method Of evaluation.

Antwort: A

Begründung:
Explanation
The correct answer is A. Select a latency metric for a request-based method of evaluation.
A latency metric measures how responsive your service is to users. For example, you can use the cloud.googleapis.com/http/server/response_latencies metric to measure the latency of HTTP requests to your service1. A request-based method of evaluation counts the number of successful requests that meet a certain criterion, such as being below a latency threshold, and compares it to the number of all requests. For example, you can define an SLI as the ratio of requests with latency below 300 ms to all requests2. A request-based method of evaluation is suitable for measuring performance over time, such as per calendar month. You can set an SLO for the SLI to be at least 90%, which means that you expect 90% of the requests to have latency below 300 ms in a month3.

66. Frage
Your organization stores all application logs from multiple Google Cloud projects in a central Cloud Logging project. Your security team wants to enforce a rule that each project team can only view their respective logs, and only the operations team can view all the logs. You need to design a solution that meets the security team's requirements, while minimizing costs. What should you do?

A. Create Identity and Access Management (IAM) roles for each project team and restrict access to the _ Default log view in their individual Google Cloud project. Grant viewer access to the operations team in the central logging project.
B. Grant each project team access to the project _ Default view in the central logging project. Grant logging viewer access to the operations team in the central logging project.
C. Create log views for each project team, and only show each project team their application logs. Grant the operations team access to the _ Al Il-jogs View in the central logging project.
D. Export logs to BigQuery tables for each project team. Grant project teams access to their tables. Grant logs writer access to the operations team in the central logging project.

Antwort: C

67. Frage
The new version of your containerized application has been tested and is ready to be deployed to production on Google Kubernetes Engine (GKE) You could not fully load-test the new version in your pre-production environment and you need to ensure that the application does not have performance problems after deployment Your deployment must be automated What should you do?

A. Deploy the application by using kubectl and set the spec. updatestrategy. type field to RollingUpdate Use Cloud Monitoring to look for performance issues, and run the kubectl rollback command if there are any issues.
B. 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
C. Deploy the application through a continuous delivery pipeline by using blue/green deployments Migrate traffic to the new version of the application and use Cloud Monitoring to look for performance issues
D. Deploy the application by using kubectl and use Config Connector to slowly ramp up traffic between versions. Use Cloud Monitoring to look for performance issues

Antwort: B

Begründung:
Explanation
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.

68. Frage
Your company is using HTTPS requests to trigger a public Cloud Run-hosted service accessible at the https://booking-engine-abcdef .a.run.app URL You need to give developers the ability to test the latest revisions of the service before the service is exposed to customers What should you do?

A. Pass the curl -K "Authorization: Hearer S(gclcud auth print-identity-token)" auth token Use the https: / /booking-engine-abcdef. a. run. app URL to test privately
B. Runthegcioud run services update-traffic booking-engine -to-revisions LATEST*! command Use the ht tps: //booking-engine-abcdef. a. run. ape URL for testing
C. Grant the roles/run. invoker role to the developers testing the booking-engine service Use the https: //booking-engine-abcdef. private. run. app URL for testing
D. Runthegcioud run deploy booking-engine -no-traffic --ag dev command Use the https://dev----booking-engine-abcdef. a. run. app URL for testing

Antwort: B

Begründung:
The best option for securing the CI/CD deployment pipeline is to configure vulnerability analysis with Artifact Registry and Binary Authorization. Vulnerability analysis is a feature that allows you to scan container images for known vulnerabilities and security issues. You can use vulnerability analysis with Artifact Registry, which is a service that allows you to store and manage container images and other artifacts. By using vulnerability analysis with Artifact Registry, you can ensure that your container images are scanned for vulnerabilities before they are deployed. Binary Authorization is a feature that allows you to enforce signature-based validation when deploying container images. You can use Binary Authorization with Cloud Build, which is a service that allows you to build and deploy container images. By using Binary Authorization with Cloud Build, you can ensure that only authorized and verified container images are deployed to your environment.

69. Frage
You support an application deployed on Compute Engine. The application connects to a Cloud SQL instance to store and retrieve data. After an update to the application, users report errors showing database timeout messages. The number of concurrent active users remained stable. You need to find the most probable cause of the database timeout. What should you do?

A. Use Stackdriver Profiler to visualize the resources utilization throughout the application.
B. Check the serial port logs of the Compute Engine instance.
C. Use Cloud Security Scanner to see whether your Cloud SQL is under a Distributed Denial of Service (DDoS) attack.
D. Determine whether there is an increased number of connections to the Cloud SQL instance.

Antwort: D

Begründung:
Explanation
The most probable cause of the database timeout is an increased number of connections to the Cloud SQL instance. This could happen if the application does not close connections properly or if it creates too many connections at once. You can check the number of connections to the Cloud SQL instance using Cloud Monitoring or Cloud SQL Admin API .

70. Frage
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