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

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

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q116-Q121):

NEW QUESTION # 116
You support a high-traffic web application that runs on Google Cloud Platform (GCP). You need to measure application reliability from a user perspective without making any engineering changes to it. What should you do?
Choose 2 answers

A. Create new synthetic clients to simulate a user journey using the application.
B. Modify the code to capture additional information for user interaction.
C. Review current application metrics and add new ones as needed.
D. Analyze the web proxy logs only and capture response time of each request.
E. Use current and historic Request Logs to trace customer interaction with the application.

Answer: A,B

NEW QUESTION # 117
You deploy a new release of an internal application during a weekend maintenance window when there is minimal user traffic. After the window ends, you learn that one of the new features isn't working as expected in the production environment. After an extended outage, you roll back the new release and deploy a fix. You want to modify your release process to reduce the mean time to recovery so you can avoid extended outages in the future. What should you do?
Choose 2 answers

A. Require developers to run automated integration tests on their local development environments before release.
B. Integrate a code linting tool to validate coding standards before any code is accepted into the repository.
C. Before merging new code, require 2 different peers to review the code changes.
D. Configure a CI server. Add a suite of unit tests to your code and have your CI server run them on commit and verify any changes.
E. Adopt the blue/green deployment strategy when releasing new code via a CD server.

Answer: D,E

NEW QUESTION # 118
Your organization is using Helm to package containerized applications Your applications reference both public and private charts Your security team flagged that using a public Helm repository as a dependency is a risk You want to manage all charts uniformly, with native access control and VPC Service Controls What should you do?

A. Configure a Helm chart repository server to run in Google Kubernetes Engine (GKE) with Cloud Storage bucket as the storage backend
B. Store public and private charts in OCI format by using Artifact Registry
C. Store public and private charts by using Git repository Configure Cloud Build to synchronize contents of the repository into a Cloud Storage bucket Connect Helm to the bucket by using https: //
[bucket] .srorage.googleapis.com/
[holnchart] as the Helm repository
D. Store public and private charts by using GitHub Enterprise with Google Workspace as the identity provider

Answer: B

NEW QUESTION # 119
You are creating Cloud Logging sinks to export log entries from Cloud Logging to BigQuery for future analysis Your organization has a Google Cloud folder named Dev that contains development projects and a folder named Prod that contains production projects Log entries for development projects must be exported to dev_dataset. and log entries for production projects must be exported to prod_datasetYou need to minimize the number of log sinks created and you want to ensure that the log sinks apply to future projects What should you do?

A. Create an aggregated Iog sink in the Dev and Prod folders
B. Create a log sink in each project
C. Create a single aggregated log sink at the organization level.
D. Create two aggregated log sinks at the organization level, and filter by project ID

Answer: C

NEW QUESTION # 120
You are the on-call Site Reliability Engineer for a microservice that is deployed to a Google Kubernetes Engine (GKE) Autopilot cluster. Your company runs an online store that publishes order messages to Pub/Sub and a microservice receives these messages and updates stock information in the warehousing system. A sales event caused an increase in orders, and the stock information is not being updated quickly enough. This is causing a large number of orders to be accepted for products that are out of stock You check the metrics for the microservice and compare them to typical levels.

You need to ensure that the warehouse system accurately reflects product inventory at the time orders are placed and minimize the impact on customers What should you do?

A. Increase the Pod CPU and memory limits
B. Decrease the acknowledgment deadline on the subscription
C. Add a virtual queue to the online store that allows typical traffic levels
D. Increase the number of Pod replicas

Answer: D

Explanation:
The best option for ensuring that the warehouse system accurately reflects product inventory at the time orders are placed and minimizing the impact on customers is to increase the number of Pod replicas. Increasing the number of Pod replicas will increase the scalability and availability of your microservice, which will allow it to handle more Pub/Sub messages and update stock information faster. This way, you can reduce the backlog of undelivered messages and oldest unacknowledged message age, which are causing delays in updating product inventory. You can use Horizontal Pod Autoscaler or Cloud Monitoring metrics-based autoscaling to automatically adjust the number of Pod replicas based on load or custom metrics.

NEW QUESTION # 121
......

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