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

Section	Weight	Objectives
Topic 1: Implementing observability and troubleshooting	20%	- Optimize observability strategy 1. Integrate observability into development 2. Reduce noise and prioritize signals 3. Scale monitoring for large environments - Build monitoring and logging systems 1. Create dashboards and alerts 2. Collect and analyze metrics, logs, traces 3. Use Cloud Monitoring, Cloud Logging, Cloud Trace - Troubleshoot complex issues 1. Trace requests end-to-end 2. Analyze performance and latency 3. Isolate problems across layers
Topic 2: Applying site reliability engineering practices	18%	- Manage incidents and outages 1. Perform postmortems and root cause analysis 2. Detect and respond to issues quickly 3. Implement improvements to prevent recurrence - 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. Automate remediation and self-healing 2. Plan redundancy and fault tolerance 3. Test failure scenarios and recovery
Topic 3: Building and implementing CI/CD pipelines	25%	- Implement and secure pipelines 1. Manage pipeline secrets and credentials 2. Enforce security scanning and compliance checks 3. Automate build, test, and deployment stages - Optimize and maintain pipelines 1. Handle rollbacks and versioning 2. Improve speed and reliability 3. Monitor pipeline performance - Design CI/CD pipelines 1. Support hybrid/multi-cloud deployment 2. Plan pipelines for apps, infrastructure, ML workloads 3. Integrate artifact management
Topic 4: Bootstrapping and maintaining a Google Cloud organization	20%	- Design resource hierarchy 1. Plan shared networking and connectivity 2. Organize resources by project/folder/application 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 - Set up governance and compliance 1. Manage resource quotas and limits 2. Configure audit logging and monitoring 3. Implement organization policies
Topic 5: Optimizing performance and cost	17%	- Improve system performance 1. Optimize resource allocation 2. Tune applications and infrastructure 3. Use caching and content delivery - Balance cost, performance, and reliability 1. Optimize over time 2. Plan capacity and scaling 3. Make tradeoff decisions based on data - Apply FinOps practices 1. Monitor and analyze cloud costs 2. Right-size resources and eliminate waste 3. Use spot instances and committed use discounts

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최신 Cloud DevOps Engineer Professional-Cloud-DevOps-Engineer 무료샘플문제 (Q57-Q62):

질문 # 57
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. Create up-to-date playbooks with instructions for debugging and mitigating issues.
B. Create an alerting mechanism for your SRE team based on your system's internal behavior.
C. Ensure that full autonomy and permissions are only granted to the on-call team.
D. Ensure that all teams can modify the production environment to resolve issues.
E. Automate common tasks to analyze key impact information and intelligently suggest mitigating actions for the on-call team.

정답：A,E

설명：
Comprehensive and Detailed Explanation From General SRE Principles and Google Cloud Knowledge:
Site Reliability Engineering (SRE) emphasizes reliability, automation, and a data-driven approach to operations. The goal is to minimize the "time to detect" (TTD) and "time to resolve" (TTR) for incidents.
Option A (Ensure that full autonomy and permissions are only granted to the on-call team): While the on-call team needs appropriate permissions to act decisively during an incident, granting full autonomy and only to them can be a bottleneck and goes against the principle of least privilege if not carefully scoped. Broader teams might need specific, controlled access for their responsibilities. SRE encourages empowering teams but within a structured framework.
Option B (Automate common tasks to analyze key impact information and intelligently suggest mitigating actions for the on-call team): This is a core SRE practice. Automation reduces toil, speeds up response, and ensures consistency. Analyzing impact and suggesting mitigations helps the on-call team resolve issues faster and more effectively.
Option C (Ensure that all teams can modify the production environment to resolve issues): This is generally a bad practice and against SRE principles of controlled changes and reducing the blast radius of errors.
Production changes should be managed, audited, and ideally automated, not open to modification by all teams, as this increases the risk of unintended incidents.
Option D (Create an alerting mechanism for your SRE team based on your system's internal behavior): While alerting is crucial, SRE emphasizes alerting on symptoms that affect users (Service Level Objectives - SLOs) rather than just internal behavior or causes. Alerting solely on internal behavior can lead to alert fatigue and may not correlate directly with user impact. Good alerting focuses on user-facing impact first.
Option E (Create up-to-date playbooks with instructions for debugging and mitigating issues): Playbooks (or runbooks) are essential in SRE. They document known issues, troubleshooting steps, and mitigation procedures. Keeping them up-to-date ensures that on-call engineers can respond to incidents quickly and consistently, even for less common issues, thereby minimizing customer impact.
Therefore, automating incident response tasks (B) and maintaining clear, actionable playbooks (E) are two key SRE practices to implement for minimizing customer impact.
Reference (Based on SRE principles):
The SRE books by Google (e.g., "Site Reliability Engineering: How Google Runs Production Systems") heavily emphasize automation to reduce toil and the importance of playbooks for incident management.
Google Cloud SRE solutions: https://cloud.google.com/sre
Specifically, regarding playbooks and automation:"Playbooks should be living documents, updated regularly as systems change and new incidents provide new lessons."
"SREs aim to automate repetitive tasks (toil) to free up time for engineering projects that improve reliability."

질문 # 58
You support an application deployed on Compute Engine. The application connects to a Cloud SQL instance to store and retrieve dat a. 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. Use Cloud Security Scanner to see whether your Cloud SQL is under a Distributed Denial of Service (DDoS) attack.
C. Determine whether there is an increased number of connections to the Cloud SQL instance.
D. Check the serial port logs of the Compute Engine instance.

정답：C

질문 # 59
You are reviewing your deployment pipeline in Google Cloud Deploy You must reduce toil in the pipeline and you want to minimize the amount of time it takes to complete an end-to-end deployment What should you do?
Choose 2 answers

A. Automate promotion approvals from the development environment to the test environment
B. Add more engineers to finish the manual steps.
C. Use a script to automate the creation of the deployment pipeline in Google Cloud Deploy
D. Divide the automation steps into smaller tasks
E. Create a trigger to notify the required team to complete the next step when manual intervention is required

정답：A,E

설명：
Explanation
The best options for reducing toil in the pipeline and minimizing the amount of time it takes to complete an end-to-end deployment are to create a trigger to notify the required team to complete the next step when manual intervention is required and to automate promotion approvals from the development environment to the test environment. A trigger is a resource that initiates a deployment when an event occurs, such as a code change, a schedule, or a manual request. You can create a trigger to notify the required team to complete the next step when manual intervention is required by using Cloud Build or Cloud Functions. This way, you can reduce the waiting time and human errors in the pipeline. A promotion approval is a process that allows you to approve or reject a deployment from one environment to another, such as from development to test. You can automate promotion approvals from the development environment to the test environment by using Google Cloud Deploy or Cloud Build. This way, you can speed up the deployment process and avoid manual steps.

질문 # 60
You use Terraform to manage an application deployed to a Google Cloud environment The application runs on instances deployed by a managed instance group The Terraform code is deployed by using aCI/CD pipeline When you change the machine type on the instance template used by the managed instance group, the pipeline fails at the terraform apply stage with the following error message

You need to update the instance template and minimize disruption to the application and the number of pipeline runs What should you do?

A. Set the create_bef ore_destroy meta-argument to true in the lifecycle block on the instance template
B. Add a new instance template update the managed instance group to use the new instance template and delete the old instance template
C. Remove the managed instance group from the Terraform state file update the instance template and reimport the managed instance group.
D. Delete the managed instance group and recreate it after updating the instance template

정답：A

설명：
The best option for updating the instance template and minimizing disruption to the application and the number of pipeline runs is to set the create_before_destroy meta-argument to true in the lifecycle block on the instance template. The create_before_destroy meta-argument is a Terraform feature that specifies that a new resource should be created before destroying an existing one during an update. This way, you can avoid downtime and errors when updating a resource that is in use by another resource, such as an instance template that is used by a managed instance group. By setting the create_before_destroy meta-argument to true in the lifecycle block on the instance template, you can ensure that Terraform creates a new instance template with the updated machine type, updates the managed instance group to use the new instance template, and then deletes the old instance template.

질문 # 61
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. 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.
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. Use the default Stackdriver Kubernetes Engine Monitoring agent configuration.

정답：C

설명：
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

질문 # 62
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