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

SectionWeightObjectives
Cloud Infrastructure Automation24%- Deployment strategies
  • 1. Blue-green deployments
  • 2. Canary releases and progressive rollouts
  • 3. Rolling updates with Kubernetes
  • 4. Feature flags with Firebase Remote Config or LaunchDarkly
- Configuration management
  • 1. Implementing configuration drift detection
  • 2. Using Ansible for configuration management
  • 3. Managing secrets with Secret Manager
- Infrastructure as Code (IaC)
  • 1. Implementing with Terraform
  • 2. Implementing immutable infrastructure patterns
  • 3. Managing infrastructure modules and state
Reliability and Site Reliability Engineering (SRE)24%- Incident management
  • 1. Implementing on-call procedures
  • 2. Post-incident reviews and blameless postmortems
  • 3. Configuring automated incident response
- SLOs, SLIs, and SLAs
  • 1. Defining and implementing Service Level Objectives
  • 2. Setting appropriate Service Level Agreements
  • 3. Creating and interpreting Service Level Indicators
  • 4. Error budget policies and management
- Monitoring and observability
  • 1. Setting up alerting policies and incident management
  • 2. Implementing distributed tracing with Cloud Trace
  • 3. Creating dashboards for service health visibility
  • 4. Configuring Cloud Monitoring and Logging
Microservices Architecture14%- Container orchestration with GKE
  • 1. Configuring node pools and auto-scaling
  • 2. Managing pod lifecycle and resource quotas
  • 3. Implementing workload deployment and scaling
  • 4. Designing Kubernetes cluster architectures
- Service mesh and networking
  • 1. Implementing Anthos Service Mesh / Istio
  • 2. Implementing service-to-service authentication
  • 3. Configuring traffic management and load balancing
- API management
  • 1. Exposing APIs with Cloud Endpoints / Apigee
  • 2. Implementing API versioning strategies
Monitoring, Logging, and Debugging12%- Debugging and troubleshooting
  • 1. Using Cloud Debugger and Error Reporting
  • 2. Analyzing performance profiles with Cloud Profiler
  • 3. Debugging with Cloud Trace distributed tracing
- Cloud Operations Suite
  • 1. Creating uptime checks and alerts
  • 2. Implementing Cloud Logging with log sinks
  • 3. Configuring Cloud Monitoring metrics
- Log management
  • 1. Structuring logs for efficient querying
  • 2. Configuring log retention policies
  • 3. Implementing log-based metrics
CI/CD Pipeline Development26%- CI/CD best practices
  • 1. Implementing shift-left testing
  • 2. Configuring quality gates and code coverage
  • 3. Managing secrets in CI/CD pipelines
- Designing and implementing CI/CD pipelines
  • 1. Configuring build caching strategies
  • 2. Designing pipeline architecture (Cloud Build, Jenkins, GitLab CI, etc.)
  • 3. Setting up artifact management with Artifact Registry
  • 4. Configuring build triggers and webhooks
  • 5. Implementing build automation scripts
- Containerization and Docker
  • 1. Managing container registries
  • 2. Implementing container security best practices
  • 3. Creating optimized Docker images (multi-stage builds)

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam 認定 Professional-Cloud-DevOps-Engineer 試験問題 (Q51-Q56):

質問 # 51
You are analyzing Java applications in production. All applications have Cloud Profiler and Cloud Trace installed and configured by default. You want to determine which applications need performance tuning. What should you do?
Choose 2 answers

正解：B、E

解説：
The correct answers are A and D)
Examine the wall-clock time and the CPU time of the application. If the difference is substantial, increase the CPU resource allocation. This is a good way to determine if the application is CPU-bound, meaning that it spends more time waiting for the CPU than performing actual computation. Increasing the CPU resource allocation can improve the performance of CPU-bound applications1.
Examine the latency time, the wall-clock time, and the CPU time of the application. If the latency time is slowly burning down the error budget, and the difference between wall-clock time and CPU time is minimal, mark the application for optimization. This is a good way to determine if the application is I/O-bound, meaning that it spends more time waiting for input/output operations than performing actual computation. Increasing the CPU resource allocation will not help I/O-bound applications, and they may need optimization to reduce the number or duration of I/O operations2.
Answer B is incorrect because increasing the memory resource allocation will not help if the application is CPU-bound or I/O-bound. Memory allocation affects how much data the application can store and access in memory, but it does not affect how fast the application can process that data.
Answer C is incorrect because increasing the local disk storage allocation will not help if the application is CPU-bound or I/O-bound. Disk storage affects how much data the application can store and access on disk, but it does not affect how fast the application can process that data.
Answer E is incorrect because examining the heap usage of the application will not help to determine if the application needs performance tuning. Heap usage affects how much memory the application allocates for dynamic objects, but it does not affect how fast the application can process those objects. Moreover, low heap usage does not necessarily mean that the application is inefficient or unoptimized.


質問 # 52
You are managing an application that runs in Compute Engine The application uses a custom HTTP server to expose an API that is accessed by other applications through an internal TCP/UDP load balancer A firewall rule allows access to the API port from 0.0.0-0/0. You need to configure Cloud Logging to log each IP address that accesses the API by using the fewest number of steps What should you do Bret?

正解：B


質問 # 53
You work for a global organization and are running a monolithic application on Compute Engine You need to select the machine type for the application to use that optimizes CPU utilization by using the fewest number of steps You want to use historical system metncs to identify the machine type for the application to use You want to follow Google-recommended practices What should you do?

正解：A

解説：
The best option for selecting the machine type for the application to use that optimizes CPU utilization by using the fewest number of steps is to use the Recommender API and apply the suggested recommendations.
The Recommender API is a service that provides recommendations for optimizing your Google Cloud resources, such as Compute Engine instances, disks, and firewalls. You can use the Recommender API to get recommendations for changing the machine type of your Compute Engine instances based on historical system metrics, such as CPU utilization. You can also apply the suggested recommendations by using the Recommender API or Cloud Console. This way, you can optimize CPU utilization by using the most suitable machine type for your application with minimal effort.


質問 # 54
You support a trading application written in Python and hosted on App Engine flexible environment. You want to customize the error information being sent to Stackdriver Error Reporting. What should you do?

正解：C

解説：
https://cloud.google.com/error-reporting/docs/formatting-error-messages
https://cloud.google.com/error-reporting/docs/reference/libraries#client-libraries-install-python no need to install error reporting library on App Engine Flex.


質問 # 55
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?

正解：C


質問 # 56
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