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

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

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q206-Q211):

NEW QUESTION # 206
You are responsible for creating and modifying the Terraform templates that define your Infrastructure.
Because two new engineers will also be working on the same code, you need to define a process and adopt a tool that will prevent you from overwriting each other's code. You also want to ensure that you capture all updates in the latest version. What should you do?

A. * Store your code in a Git-based version control system.* Establish a process that allows developers to merge their own changes at the end of each day.* Package and upload code lo a versioned Cloud Storage bucket as the latest master version.
B. * Store your code in a Git-based version control system.* Establish a process that includes code reviews by peers and unit testing to ensure integrity and functionality before integration of code.* Establish a process where the fully integrated code in the repository becomes the latest master version.
C. * Store your code as text files in Google Drive in a defined folder structure that organizes the files.* At the end of each day, confirm that all changes have been captured in the files within the folder structure and create a new .zip archive with a predefined naming convention.* Upload the .zip archive to a versioned Cloud Storage bucket and accept it as the latest version.
D. * Store your code as text files in Google Drive in a defined folder structure that organizes the files.* At the end of each day. confirm that all changes have been captured in the files within the folder structure.* Rename the folder structure with a predefined naming convention that increments the version.

Answer: B

NEW QUESTION # 207
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. Use the default Stackdriver Kubernetes Engine Monitoring agent configuration.
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. 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.
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: D

NEW QUESTION # 208
Your team is preparing to launch a new API in Cloud Run. The API uses an OpenTelemetry agent to send distributed tracing data to Cloud Trace to monitor the time each request takes. The team has noticed inconsistent trace collection. You need to resolve the issue. What should you do?

A. Configure CPU to be allocated only during request processing.
B. Increase the CPU limit in Cloud Run from 2 to 4.
C. Configure CPU to be always-allocated.
D. Use an HTTP health check.

Answer: C

NEW QUESTION # 209
You use a multiple step Cloud Build pipeline to build and deploy your application to Google Kubernetes Engine (GKE). You want to integrate with a third-party monitoring platform by performing a HTTP POST of the build information to a webhook. You want to minimize the development effort. What should you do?

A. Add a new step at the end of the pipeline in Cloud Build to HTTP POST the build information to a webhook.
B. Use Stackdriver Logging to create a logs-based metric from the Cloud Buitd logs. Create an Alert with a Webhook notification type.
C. Create a Cloud Pub/Sub push subscription to the Cloud Build cloud-builds PubSub topic to HTTP POST the build information to a webhook.
D. Add logic to each Cloud Build step to HTTP POST the build information to a webhook.

Answer: A

NEW QUESTION # 210
Your company processes IOT data at scale by using Pub/Sub, App Engine standard environment, and an application written in GO. You noticed that the performance inconsistently degrades at peak load. You could not reproduce this issue on your workstation. You need to continuously monitor the application in production to identify slow paths in the code. You want to minimize performance impact and management overhead.
What should you do?

A. Configure Cloud Profiler, and initialize the cloud.go@gle.com/go/profiler library in the application.
B. Periodically run the go tool pprof command against the application instance. Analyze the results by using flame graphs.
C. Use Cloud Monitoring to assess the App Engine CPU utilization metric.
D. Install a continuous profiling tool into Compute Engine. Configure the application to send profiling data to the tool.

Answer: A

Explanation:
The correct answer is C. Configure Cloud Profiler, and initialize the cloud.google.com/go/profiler library in the application.
According to the Google Cloud documentation, Cloud Profiler is a statistical, low-overhead profiler that continuously gathers CPU usage and memory-allocation information from your production applications1.
Cloud Profiler can help you identify slow paths in your code and optimize the performance of your applications. Cloud Profiler supports applications written in Go that run on App Engine standard environment2. To use Cloud Profiler, you need to configure it in your Google Cloud project and initialize the cloud.google.com/go/profiler library in your application code3. You can then use the Cloud Profiler interface to analyze the profiling data and visualize the results by using flame graphs4. Cloud Profiler has minimal performance impact and management overhead, as it only samples a small fraction of the application activity and does not require any additional infrastructure or agents.
The other options are incorrect because they do not meet the requirements of minimizing performance impact and management overhead. Option A is incorrect because it requires installing a continuous profiling tool into Compute Engine, which is an additional infrastructure that needs to be managed and maintained. Option B is incorrect because it requires periodically running the go tool pprof command against the application instance, which is a manual and disruptive process that can affect the application performance. Option D is incorrect because it only uses Cloud Monitoring to assess the App Engine CPU utilization metric, which is not enough to identify slow paths in the code or optimize the application performance.
Reference:
Cloud Profiler documentation, Overview. Profiling Go applications, Supported environments. Profiling Go applications, Using Cloud Profiler. Analyzing data, Analyzing data.

NEW QUESTION # 211
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