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

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

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q98-Q103):

NEW QUESTION # 98
You have a set of applications running on a Google Kubernetes Engine (GKE) cluster, and you are using Stackdriver Kubernetes Engine Monitoring. You are bringing a new containerized application required by your company into production. This application is written by a third party and cannot be modified or reconfigured. The application writes its log information to /var/log/app_messages.log, and you want to send these log entries to Stackdriver Logging. What should you do?

A. Install Kubernetes on Google Compute Engine (GCE> and redeploy your applications. Then customize the built-in Stackdriver Logging configuration to tail the log file in the application's pods and write to Stackdriver Logging.
B. Deploy a Fluentd daemonset to GKE. Then create a customized input and output configuration to tail the log file in the application's pods and write to Slackdriver Logging.
C. Write a script to tail the log file within the pod and write entries to standard output. Run the script as a sidecar container with the application's pod. Configure a shared volume between the containers to allow the script to have read access to /var/log in the application container.
D. Use the default Stackdriver Kubernetes Engine Monitoring agent configuration.

Answer: B

Explanation:
https://cloud.google.com/architecture/customizing-stackdriver-logs-fluentd Besides the list of default logs that the Logging agent streams by default, you can customize the Logging agent to send additional logs to Logging or to adjust agent settings by adding input configurations. The configuration definitions in these sections apply to the fluent-plugin-google-cloud output plugin only and specify how logs are transformed and ingested into Cloud Logging.https://cloud.google.com/logging/docs
/agent/logging/configuration#configure

NEW QUESTION # 99
You support a large service with a well-defined Service Level Objective (SLO). The development team deploys new releases of the service multiple times a week. If a major incident causes the service to miss its SLO, you want the development team to shift its focus from working on features to improving service reliability. What should you do before a major incident occurs?

A. Develop an appropriate error budget policy in cooperation with all service stakeholders.
B. Add a plugin to your Jenkins pipeline that prevents new releases whenever your service is out of SLO.
C. Negotiate with the product team to always prioritize service reliability over releasing new features.
D. Negotiate with the development team to reduce the release frequency to no more than once a week.

Answer: C

NEW QUESTION # 100
Your team has an application built by using a Dockerfile. The build is executed from Cloud Build, and the resulting artifacts are stored in Artifact Registry. Your team is reporting that builds are slow. You need to increase build speed, while following Google-recommended practices. What should you do?

A. Cache the container layers of the build process to Cloud Storage. Add the most frequently modified files to the earlier stages of the build process.
B. Use the --cache-from parameter, and point to Artifact Registry. Add the most frequently modified files to the later stages of the build process.
C. Use the --cache-from parameter, and point to Artifact Registry. Add the most frequently modified files to the earlier stages of the build process.
D. Cache the container layers of the build process to Cloud Storage. Add the most frequently modified files to the later stages of the build process.

Answer: B

Explanation:
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.

NEW QUESTION # 101
You manage an application that is writing logs to Stackdriver Logging. You need to give some team members the ability to export logs. What should you do?

A. Grant the team members the IAM role of logging.configWriter on Cloud IAM.
B. Configure Access Context Manager to allow only these members to export logs.
C. Create an Organizational Policy in Cloud IAM to allow only these members to create log exports.
D. Create and grant a custom IAM role with the permissions logging.sinks.list and logging.sink.get.

Answer: A

Explanation:
https://cloud.google.com/logging/docs/access-control
The logging.configWriter role grants permissions to create, update, and delete log exports. This is the correct role to give team members who need to export logs2.

NEW QUESTION # 102
You support an application that stores product information in cached memory. For every cache miss, an entry is logged in Stackdriver Logging. You want to visualize how often a cache miss happens over time. What should you do?

A. Create a logs-based metric in Stackdriver Logging and a dashboard for that metric in Stackdriver Monitoring.
B. Link Stackdriver Logging as a source in Google Data Studio. Filler (he logs on the cache misses.
C. Configure Stackdriver Profiler to identify and visualize when the cache misses occur based on the logs.
D. Configure BigOuery as a sink for Stackdriver Logging. Create a scheduled query to filter the cache miss logs and write them to a separate table

Answer: A

Explanation:
https://cloud.google.com/logging/docs/logs-based-metrics#counter-metric

NEW QUESTION # 103
......

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