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

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

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q180-Q185):

NEW QUESTION # 180
You support a popular mobile game application deployed on Google Kubernetes Engine (GKE) across several Google Cloud regions. Each region has multiple Kubernetes clusters. You receive a report that none of the users in a specific region can connect to the application. You want to resolve the incident while following Site Reliability Engineering practices. What should you do first?

A. Use Stackdriver Logging to filter on the clusters in the affected region, and inspect error messages in the logs.
B. Reroute the user traffic from the affected region to other regions that don't report issues.
C. Add an extra node pool that consists of high memory and high CPU machine type instances to the cluster.
D. Use Stackdriver Monitoring to check for a spike in CPU or memory usage for the affected region.

Answer: B

Explanation:
Google always aims to first stop the impact of an incident, and then find the root cause (unless the root cause just happens to be identified early on).

NEW QUESTION # 181
You are managing an application that exposes an HTTP endpoint without using a load balancer. The latency of the HTTP responses is important for the user experience. You want to understand what HTTP latencies all of your users are experiencing. You use Stackdriver Monitoring. What should you do?

A. * In your application, create a metric with a metricKind set to DELTA and a valueType set to DOUBLE.
* In Stackdriver's Metrics Explorer, use a Slacked Bar graph to visualize the metric.
B. * In your application, create a metric with a metricKind set to CUMULATIVE and a valueType set to DOUBLE.* In Stackdriver's Metrics Explorer, use a Line graph to visualize the metric.
C. * In your application, create a metric with a metricKind. set toMETRlc_KIND_UNSPECIFIEDanda valueType set to INT64.* In Stackdriver's Metrics Explorer, use a Stacked Area graph to visualize the metric.
D. * In your application, create a metric with a metricKind set to gauge and a valueType set to distribution.
* In Stackdriver's Metrics Explorer, use a Heatmap graph to visualize the metric.

Answer: D

Explanation:
https://sre.google/workbook/implementing-slos/
https://cloud.google.com/architecture/adopting-slos/
Latency is commonly measured as a distribution. Given a distribution, you can measure various percentiles.
For example, you might measure the number of requests that are slower than the historical 99th percentile.

NEW QUESTION # 182
Your company is migrating its production systems to Google Cloud. You need to implement site reliability engineering (SRE) practices during the migration to minimize customer impact from potential future incidents. Which two SRE practices should you implement?
Choose 2 answers

A. Ensure that full autonomy and permissions are only granted to the on-call team.
B. Create up-to-date playbooks with instructions for debugging and mitigating issues.
C. Ensure that all teams can modify the production environment to resolve issues.
D. Create an alerting mechanism for your SRE team based on your system's internal behavior.
E. Automate common tasks to analyze key impact information and intelligently suggest mitigating actions for the on-call team.

Answer: B,E

NEW QUESTION # 183
You use Spinnaker to deploy your application and have created a canary deployment stage in the pipeline. Your application has an in-memory cache that loads objects at start time. You want to automate the comparison of the canary version against the production version. How should you configure the canary analysis?

A. Compare the canary with the existing deployment of the current production version.
B. Compare the canary with the average performance of a sliding window of previous production versions.
C. Compare the canary with a new deployment of the previous production version.
D. Compare the canary with a new deployment of the current production version.

Answer: B

NEW QUESTION # 184
Your organization has a containerized web application that runs on-premises As part of the migration plan to Google Cloud you need to select a deployment strategy and platform that meets the following acceptance criteria
1 The platform must be able to direct traffic from Android devices to an Android-specific microservice
2 The platform must allow for arbitrary percentage-based traffic splitting
3 The deployment strategy must allow for continuous testing of multiple versions of any microservice What should you do?

A. Deploy the canary release to Google Kubernetes Engine with Anthos Sen/ice Mesh Use traffic splitting to direct 10% of user traffic to the new version based on the user-agent header configured in the virtual service
B. Deploy the canary release of the application to Cloud Run Use traffic splitting to direct 10% of user traffic to the canary release based on the revision tag
C. Deploy the canary release of the application to App Engine Use traffic splitting to direct a subset of user traffic to the new version based on the IP address
D. Deploy the canary release of the application to Compute Engine Use Anthos Service Mesh with Compute Engine to direct 10% of user traffic to the canary release by configuring the virtual service.

Answer: A

Explanation:
The best option for deploying a containerized web application to Google Cloud with the given acceptance criteria is to use Google Kubernetes Engine (GKE) with Anthos Service Mesh. GKE is a managed service for running Kubernetes clusters on Google Cloud, and Anthos Service Mesh is a service mesh that provides observability, traffic management, and security features for microservices. With Anthos Service Mesh, you can use traffic splitting to direct traffic from Android devices to an Android-specific microservice by configuring the user-agent header in the virtual service. You can also use traffic splitting to direct arbitrary percentage-based traffic to different versions of any microservice for continuous testing. For example, you can use a canary release strategy to direct 10% of user traffic to a new version of a microservice and monitor its performance and reliability.

NEW QUESTION # 185
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