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

Section	Weight	Objectives
Topic 1: Monitoring, Logging, and Debugging	12%	- Debugging and troubleshooting 1. Analyzing performance profiles with Cloud Profiler 2. Debugging with Cloud Trace distributed tracing 3. Using Cloud Debugger and Error Reporting - Cloud Operations Suite 1. Configuring Cloud Monitoring metrics 2. Creating uptime checks and alerts 3. Implementing Cloud Logging with log sinks - Log management 1. Structuring logs for efficient querying 2. Configuring log retention policies 3. Implementing log-based metrics
Topic 2: Reliability and Site Reliability Engineering (SRE)	24%	- Monitoring and observability 1. Implementing distributed tracing with Cloud Trace 2. Creating dashboards for service health visibility 3. Setting up alerting policies and incident management 4. Configuring Cloud Monitoring and Logging - Incident management 1. Implementing on-call procedures 2. Configuring automated incident response 3. Post-incident reviews and blameless postmortems - SLOs, SLIs, and SLAs 1. Setting appropriate Service Level Agreements 2. Defining and implementing Service Level Objectives 3. Error budget policies and management 4. Creating and interpreting Service Level Indicators
Topic 3: CI/CD Pipeline Development	26%	- CI/CD best practices 1. Implementing shift-left testing 2. Configuring quality gates and code coverage 3. Managing secrets in CI/CD pipelines - Containerization and Docker 1. Creating optimized Docker images (multi-stage builds) 2. Managing container registries 3. Implementing container security best practices - Designing and implementing CI/CD pipelines 1. Designing pipeline architecture (Cloud Build, Jenkins, GitLab CI, etc.) 2. Configuring build triggers and webhooks 3. Implementing build automation scripts 4. Configuring build caching strategies 5. Setting up artifact management with Artifact Registry
Topic 4: Cloud Infrastructure Automation	24%	- Deployment strategies 1. Rolling updates with Kubernetes 2. Blue-green deployments 3. Canary releases and progressive rollouts 4. Feature flags with Firebase Remote Config or LaunchDarkly - Infrastructure as Code (IaC) 1. Implementing immutable infrastructure patterns 2. Implementing with Terraform 3. Managing infrastructure modules and state - Configuration management 1. Using Ansible for configuration management 2. Managing secrets with Secret Manager 3. Implementing configuration drift detection
Topic 5: Microservices Architecture	14%	- Container orchestration with GKE 1. Implementing workload deployment and scaling 2. Configuring node pools and auto-scaling 3. Designing Kubernetes cluster architectures 4. Managing pod lifecycle and resource quotas - API management 1. Implementing API versioning strategies 2. Exposing APIs with Cloud Endpoints / Apigee - Service mesh and networking 1. Configuring traffic management and load balancing 2. Implementing Anthos Service Mesh / Istio 3. Implementing service-to-service authentication

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最新的 Cloud DevOps Engineer Professional-Cloud-DevOps-Engineer 免費考試真題 (Q77-Q82):

問題 #77
You are configuring your CI/CD pipeline natively on Google Cloud. You want builds in a pre-production Google Kubernetes Engine (GKE) environment to be automatically load-tested before being promoted to the production GKE environment. You need to ensure that only builds that have passed this test are deployed to production. You want to follow Google-recommended practices. How should you configure this pipeline with Binary Authorization?

A. Create an attestation for the builds that pass the load test by requiring the lead quality assurance engineer to sign the attestation by using a key stored in Cloud Key Management Service (Cloud KMS).
B. Create an attestation for the builds that pass the load test by requiring the lead quality assurance engineer to sign the attestation by using their personal private key.
C. Create an attestation for the builds that pass the load test by using a private key stored in Cloud Key Management Service (Cloud KMS) authenticated through Workload Identity.
D. Create an attestation for the builds that pass the load test by using a private key stored in Cloud Key Management Service (Cloud KMS) with a service account JSON key stored as a Kubernetes Secret.

答案：C

解題說明：
The correct answer is B. Create an attestation for the builds that pass the load test by using a private key stored in Cloud Key Management Service (Cloud KMS) authenticated through Workload Identity.
According to the Google Cloud documentation, Binary Authorization is a deploy-time security control that ensures only trusted container images are deployed on Google Kubernetes Engine (GKE) or Cloud Run1.
Binary Authorization uses attestations to certify that a specific image has completed a previous stage in the CI
/CD pipeline, such as passing a load test2.Attestations are signed by private keys that are associated with attestors, which are entities that verify the attestations3.To follow Google-recommended practices, you should store your private keys in Cloud Key ManagementService (Cloud KMS), which is a secure and scalable service for managing cryptographic keys4.You should also use Workload Identity, which is a feature that allows Kubernetes service accounts to act as Google service accounts, to authenticate to Cloud KMS and sign attestations without having to manage or expose service account keys5.
The other options are incorrect because they do not follow Google-recommended practices. Option A and option D require human intervention to sign the attestations, which is not scalable or automated. Option C exposes the service account JSON key as a Kubernetes Secret, which is less secure than using Workload Identity.

問題 #78
You are responsible for the reliability of a high-volume enterprise application. A large number of users report that an important subset of the application's functionality - a data intensive reporting feature - is consistently failing with an HTTP 500 error. When you investigate your application's dashboards, you notice a strong correlation between the failures and a metric that represents the size of an internal queue used for generating reports. You trace the failures to a reporting backend that is experiencing high I/O wait times. You quickly fix the issue by resizing the backend's persistent disk (PD). How you need to create an availability Service Level Indicator (SLI) for the report generation feature. How would you define it?

A. As the reporting backend PD throughout capacity compared to a known-good threshold
B. As the application's report generation queue size compared to a known-good threshold
C. As the proportion of report generation requests that result in a successful response
D. As the I/O wait times aggregated across all report generation backends

答案：C

解題說明：
Explanation
According to SRE Workbook, one of potential SLI is as below:
* Type of service: Request-driven
* Type of SLI: Availability
* Description: The proportion of requests that resulted in a successful response.
https://sre.google/workbook/implementing-slos/

問題 #79
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 logic to each Cloud Build step to HTTP POST the build information to a webhook.
B. Add a new step at the end of the pipeline in Cloud Build to HTTP POST the build information to a webhook.
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. Use Stackdriver Logging to create a logs-based metric from the Cloud Buitd logs. Create an Alert with a Webhook notification type.

答案：C

問題 #80
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 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.
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 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.

答案：D

問題 #81
Your team has recently deployed an NGINX-based application into Google Kubernetes Engine (GKE) and has exposed it to the public via an HTTP Google Cloud Load Balancer (GCLB) ingress. You want to scale the deployment of the application's frontend using an appropriate Service Level Indicator (SLI). What should you do?

A. Configure the horizontal pod autoscaler to use the average response time from the Liveness and Readiness probes.
B. Install the Stackdriver custom metrics adapter and configure a horizontal pod autoscaler to use the number of requests provided by the GCLB.
C. Expose the NGINX stats endpoint and configure the horizontal pod autoscaler to use the request metrics exposed by the NGINX deployment.
D. Configure the vertical pod autoscaler in GKE and enable the cluster autoscaler to scale the cluster as pods expand.

答案：D

問題 #82
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