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

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

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q75-Q80):

NEW QUESTION # 75
You are running a web application deployed to a Compute Engine managed instance group Ops Agent is installed on all instances You recently noticed suspicious activity from a specific IP address You need to configure Cloud Monitoring to view the number of requests from that specific IP address with minimal operational overhead. What should you do?

A. Create a script to scrape the web server log Export the IP address request metrics to the Cloud Monitoring API
B. Configure the Ops Agent with a metrics receiver
C. Configure the Ops Agent with a logging receiver Create a logs-based metric
D. Update the application to export the IP address request metrics to the Cloud Monitoring API

Answer: C

NEW QUESTION # 76
You are currently planning how to display Cloud Monitoring metrics for your organization's Google Cloud projects. Your organization has three folders and six projects:

You want to configure Cloud Monitoring dashboards lo only display metrics from the projects within one folder You need to ensure that the dashboards do not display metrics from projects in the other folders You want to follow Google-recommended practices What should you do?

A. Use the current app-one-prod project as the scoping project
B. Use the current app-one-dev, app-one-staging and app-one-prod projects as the scoping project for each folder
C. Create new scoping projects for each folder
D. Create a single new scoping project

Answer: C

Explanation:
The best option for configuring Cloud Monitoring dashboards to only display metrics from the projects within one folder is to create new scoping projects for each folder. A scoping project is a project that defines which resources are monitored by Cloud Monitoring. You can create new scoping projects for each folder by using the gcloud monitoring register-project command. This way, you can associate each scoping project with a folder and only monitor the resources within that folder. You can then configure Cloud Monitoring dashboards to use the scoping projects as data sources and only display metrics from the projects within one folder.

NEW QUESTION # 77
Your organization is running multiple Google Kubernetes Engine (GKE) clusters in a project. You need to design a highly-available solution to collect and query both domain-specific workload metrics and GKE default metrics across all clusters, while minimizing operational overhead. What should you do?

A. Use Prometheus Operator to install Prometheus in every cluster and scrape the metrics. Ensure that a Thanos sidecar is enabled on every Prometheus instance. Configure Thanos in the central cluster. Query the central Thanos instance.
B. Use Prometheus Operator to install Prometheus in every cluster and scrape the metrics. Configure remote-write to one central Prometheus. Query the central Prometheus instance.
C. Enable managed collection on every GKE cluster. Query the metrics in Cloud Monitoring.
D. Enable managed collection on every GKE cluster. Query the metrics in BigQuery.

Answer: C

Explanation:
Google recommends using managed collection for GKE monitoring to reduce operational overhead. This sends Kubernetes metrics and custom workload metrics directly to Cloud Monitoring.
"You can enable Managed Service for Prometheus, which provides a fully managed Prometheus-compatible monitoring solution integrated with Cloud Monitoring."
- Managed Collection in GKE
"It automatically collects and stores metrics in Cloud Monitoring with no need to manage a Prometheus server."
- Google Cloud Managed Prometheus
It's scalable, cost-effective, and requires no additional infrastructure like Thanos or Prometheus federation.

NEW QUESTION # 78
Your applications performance in Google Cloud has degraded since the last release You suspect that downstream dependencies might be causing some requests to take longer to complete You need to investigate the issue with your application to determine the cause What should you do?

A. Configure Cloud Profiler in your application
B. Configure Google Cloud Managed Service for Prometheus in your application
C. Configure Cloud Trace in your application
D. Configure Error Reporting in your application

Answer: C

NEW QUESTION # 79
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 application's report generation queue size compared to a known-good threshold
B. As the reporting backend PD throughout capacity 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

Answer: C

Explanation:
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/

NEW QUESTION # 80
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