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

Section	Weight	Objectives
Building and implementing CI/CD pipelines	25%	- Implement and secure pipelines 1. Enforce security scanning and compliance checks 2. Manage pipeline secrets and credentials 3. Automate build, test, and deployment stages - Design CI/CD pipelines 1. Plan pipelines for apps, infrastructure, ML workloads 2. Support hybrid/multi-cloud deployment 3. Integrate artifact management - Optimize and maintain pipelines 1. Handle rollbacks and versioning 2. Monitor pipeline performance 3. Improve speed and reliability
Implementing observability and troubleshooting	20%	- Troubleshoot complex issues 1. Analyze performance and latency 2. Trace requests end-to-end 3. Isolate problems across layers - Build monitoring and logging systems 1. Use Cloud Monitoring, Cloud Logging, Cloud Trace 2. Create dashboards and alerts 3. Collect and analyze metrics, logs, traces - Optimize observability strategy 1. Reduce noise and prioritize signals 2. Integrate observability into development 3. Scale monitoring for large environments
Optimizing performance and cost	17%	- Balance cost, performance, and reliability 1. Make tradeoff decisions based on data 2. Plan capacity and scaling 3. Optimize over time - Apply FinOps practices 1. Monitor and analyze cloud costs 2. Right-size resources and eliminate waste 3. Use spot instances and committed use discounts - Improve system performance 1. Optimize resource allocation 2. Use caching and content delivery 3. Tune applications and infrastructure
Bootstrapping and maintaining a Google Cloud organization	20%	- Design resource hierarchy 1. Define IAM policies and access control 2. Organize resources by project/folder/application 3. Plan shared networking and connectivity - Set up governance and compliance 1. Configure audit logging and monitoring 2. Implement organization policies 3. Manage resource quotas and limits - Manage configuration and deployment 1. Use Infrastructure as Code tools 2. Enforce consistency across environments 3. Automate resource provisioning
Applying site reliability engineering practices	18%	- Define reliability goals 1. Align reliability with business needs 2. Establish SLIs, SLOs, SLAs 3. Use error budgets to balance change and stability - Manage incidents and outages 1. Perform postmortems and root cause analysis 2. Implement improvements to prevent recurrence 3. Detect and respond to issues quickly - Design for resilience 1. Test failure scenarios and recovery 2. Plan redundancy and fault tolerance 3. Automate remediation and self-healing

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q130-Q135):

NEW QUESTION # 130
You have deployed a fleet Of Compute Engine instances in Google Cloud. You need to ensure that monitoring metrics and logs for the instances are visible in Cloud Logging and Cloud Monitoring by your company's operations and cyber security teams. You need to grant the required roles for the Compute Engine service account by using Identity and Access Management (IAM) while following the principle of least privilege. What should you do?

A. Grant the logging.editor and monitoring.metricwriter roles to the Compute Engine service accounts.
B. Grant the logging. logwriter and monitoring. editor roles to the Compute Engine service accounts.
C. Grant the Logging. admin and monitoring . editor roles to the Compute Engine service accounts.
D. Grant the logging. logWriter and monitoring. metricWriter roles to the Compute Engine service accounts.

Answer: A

NEW QUESTION # 131
You are ready to deploy a new feature of a web-based application to production. You want to use Google Kubernetes Engine (GKE) to perform a phased rollout to half of the web server pods.
What should you do?

A. Use a replica set in the deployment specification.
B. Use Node taints with NoExecute.
C. Use a stateful set with parallel pod management policy.
D. Use a partitioned rolling update.

Answer: D

Explanation:
Explanation
https://medium.com/velotio-perspectives/exploring-upgrade-strategies-for-stateful-sets-in-kubernetes-c02b8286f

NEW QUESTION # 132
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. 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.
B. 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.
C. Use the default Stackdriver Kubernetes Engine Monitoring agent configuration.
D. 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.

Answer: A

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 # 133
Your team is designing a new application for deployment into Google Kubernetes Engine (GKE). You need to set up monitoring to collect and aggregate various application-level metrics in a centralized location. You want to use Google Cloud Platform services while minimizing the amount of work required to set up monitoring. What should you do?

A. Publish various melrics from the application directly to the Slackdriver Monitoring API, and then observe these custom metrics in Stackdriver.
B. Install the OpenTelemetry client libraries in the application, configure Stackdriver as the export destination for the metrics, and then observe the application's metrics in Stackdriver.
C. Install the Cloud Pub/Sub client libraries, push various metrics from the application to various topics, and then observe the aggregated metrics in Stackdriver.
D. Emit all metrics in the form of application-specific log messages, pass these messages from the containers to the Stackdriver logging collector, and then observe metrics in Stackdriver.

Answer: A

NEW QUESTION # 134
You are designing a deployment technique for your applications on Google Cloud. As part Of your deployment planning, you want to use live traffic to gather performance metrics for new versions Of your applications. You need to test against the full production load before your applications are launched. What should you do?

A. Use canary testing with continuous deployment.
B. Use shadow testing with continuous deployment.
C. Use canary testing with rolling updates deployment,
D. Use A/B testing with blue/green deployment.

Answer: B

Explanation:
The correct answer is B, Use shadow testing with continuous deployment.
Shadow testing is a deployment technique that involves routing a copy of the live traffic to a new version of the application, without affecting the production environment. This way, you can gather performance metrics and compare them with the current version, without exposing the new version to the users. Shadow testing can help you test against the full production load and identify any issues or bottlenecks before launching the new version. You can use continuous deployment to automate the process of deploying the new version after it passes the shadow testing.
Reference:
Application deployment and testing strategies, Testing strategies, Shadow test pattern.

NEW QUESTION # 135
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