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

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

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Professional-Cloud-DevOps-Engineer Test Quiz: Google Cloud Certified - Professional Cloud DevOps Engineer Exam & Professional-Cloud-DevOps-Engineer Actual Exam & Professional-Cloud-DevOps-Engineer Exam Training

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q90-Q95):

NEW QUESTION # 90
You manage several production systems that run on Compute Engine in the same Google Cloud Platform (GCP) project. Each system has its own set of dedicated Compute Engine instances. You want to know how must it costs to run each of the systems. What should you do?

A. Name each virtual machine (VM) after the system it runs. Set up a usage report export to a Cloud Storage bucket. Configure the bucket as a source in BigQuery to query costs based on VM name.
B. Enrich all instances with metadata specific to the system they run. Configure Stackdriver Logging to export to BigQuery, and query costs based on the metadata.
C. Assign all instances a label specific to the system they run. Configure BigQuery billing export and query costs per label.
D. In the Google Cloud Platform Console, use the Cost Breakdown section to visualize the costs per system.

Answer: A

NEW QUESTION # 91
Your organization recently adopted a container-based workflow for application development. Your team develops numerous applications that are deployed continuously through an automated build pipeline to a Kubernetes cluster in the production environment. The security auditor is concerned that developers or operators could circumvent automated testing and push code changes to production without approval. What should you do to enforce approvals?

A. Configure the build system with protected branches that require pull request approval.
B. Leverage Kubernetes Role-Based Access Control (RBAC) to restrict access to only approved users.
C. Enable binary authorization inside the Kubernetes cluster and configure the build pipeline as an attestor.
D. Use an Admission Controller to verify that incoming requests originate from approved sources.

Answer: C

NEW QUESTION # 92
You support an application that stores product information in cached memory. For every cache miss, an entry is logged in Stackdriver Logging. You want to visualize how often a cache miss happens over time. What should you do?

A. Link Stackdriver Logging as a source in Google Data Studio. Filler (he logs on the cache misses.
B. Create a logs-based metric in Stackdriver Logging and a dashboard for that metric in Stackdriver Monitoring.
C. Configure Stackdriver Profiler to identify and visualize when the cache misses occur based on the logs.
D. Configure BigOuery as a sink for Stackdriver Logging. Create a scheduled query to filter the cache miss logs and write them to a separate table

Answer: B

Explanation:
https://cloud.google.com/logging/docs/logs-based-metrics#counter-metric

NEW QUESTION # 93
You recently migrated an ecommerce application to Google Cloud. You now need to prepare the application for the upcoming peak traffic season. You want to follow Google-recommended practices. What should you do first to prepare for the busy season?

A. Migrate the application to Cloud Run, and use autoscaling.
B. Pre-provision the additional compute power that was used last season, and expect growth.
C. Create a Terraform configuration for the application's underlying infrastructure to quickly deploy to additional regions.
D. Load test the application to profile its performance for scaling.

Answer: D

Explanation:
The first thing you should do to prepare your ecommerce application for the upcoming peak traffic season is to load test the application to profile its performance for scaling. Load testing is a process of simulating high traffic or user demand on your application and measuring how it responds.Load testing can help you identify any bottlenecks, errors, or performance issues that might affect your application during the busy season1.Load testing can also help you determine the optimal scaling strategy for your application, such as horizontal scaling (adding more instances) or vertical scaling (adding more resources to each instance)2.
There are different tools and methods for load testing your ecommerce application on Google Cloud, depending on the type and complexity of your application.For example, you can use Cloud Load Balancing to distribute traffic across multiple instances of your application, and use Cloud Monitoring to measure the latency, throughput, and error rate of your application3.You can also use Cloud Functions or Cloud Run to create serverless load generators that can simulate user requests and send them to your application4.
Alternatively, you can use third-party tools such as Apache JMeter or Locust to create and run load tests on your application.
By load testing your ecommerce application before the peak traffic season, you can ensure that your application is ready to handle the expected load and provide a good user experience. You can also use the results of your load tests to plan and implement other steps to prepare your application for the busy season, such as migrating to a more scalable platform, creating a Terraform configuration for deploying to additional regions, or pre-provisioning additional compute power.
References:
1:Load Testing 101: How To Test Website Performance | BlazeMeter
2: Scaling applications | Google Cloud
3:Load testing using Google Cloud | Solutions | Google Cloud
4: Serverless load testing using Cloud Functions | Solutions | Google Cloud

NEW QUESTION # 94
You are part of an organization that follows SRE practices and principles. You are taking over the management of a new service from the Development Team, and you conduct a Production Readiness Review (PRR). After the PRR analysis phase, you determine that the service cannot currently meet its Service Level Objectives (SLOs). You want to ensure that the service can meet its SLOs in production. What should you do next?

A. Notify the development team that they will have to provide production support for the service.
B. Adjust the SLO targets to be achievable by the service so you can bring it into production.
C. Identify recommended reliability improvements to the service to be completed before handover.
D. Bring the service into production with no SLOs and build them when you have collected operational data.

Answer: A

NEW QUESTION # 95
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