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

Section	Weight	Objectives
Topic 1: Applying site reliability engineering practices	18%	- Manage incidents and outages 1. Perform postmortems and root cause analysis 2. Detect and respond to issues quickly 3. Implement improvements to prevent recurrence - Define reliability goals 1. Use error budgets to balance change and stability 2. Establish SLIs, SLOs, SLAs 3. Align reliability with business needs - Design for resilience 1. Automate remediation and self-healing 2. Plan redundancy and fault tolerance 3. Test failure scenarios and recovery
Topic 2: Optimizing performance and cost	17%	- Balance cost, performance, and reliability 1. Plan capacity and scaling 2. Make tradeoff decisions based on data 3. Optimize over time - Apply FinOps practices 1. Use spot instances and committed use discounts 2. Right-size resources and eliminate waste 3. Monitor and analyze cloud costs - Improve system performance 1. Use caching and content delivery 2. Tune applications and infrastructure 3. Optimize resource allocation
Topic 3: Building and implementing CI/CD pipelines	25%	- Optimize and maintain pipelines 1. Monitor pipeline performance 2. Improve speed and reliability 3. Handle rollbacks and versioning - Implement and secure pipelines 1. Automate build, test, and deployment stages 2. Manage pipeline secrets and credentials 3. Enforce security scanning and compliance checks - Design CI/CD pipelines 1. Integrate artifact management 2. Plan pipelines for apps, infrastructure, ML workloads 3. Support hybrid/multi-cloud deployment
Topic 4: Implementing observability and troubleshooting	20%	- Optimize observability strategy 1. Scale monitoring for large environments 2. Integrate observability into development 3. Reduce noise and prioritize signals - Build monitoring and logging systems 1. Create dashboards and alerts 2. Use Cloud Monitoring, Cloud Logging, Cloud Trace 3. Collect and analyze metrics, logs, traces - Troubleshoot complex issues 1. Analyze performance and latency 2. Isolate problems across layers 3. Trace requests end-to-end
Topic 5: Bootstrapping and maintaining a Google Cloud organization	20%	- Manage configuration and deployment 1. Automate resource provisioning 2. Use Infrastructure as Code tools 3. Enforce consistency across environments - Set up governance and compliance 1. Manage resource quotas and limits 2. Configure audit logging and monitoring 3. Implement organization policies - Design resource hierarchy 1. Organize resources by project/folder/application 2. Plan shared networking and connectivity 3. Define IAM policies and access control

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q101-Q106):

NEW QUESTION # 101
You need to build a CI/CD pipeline for a containerized application in Google Cloud Your development team uses a central Git repository for trunk-based development You want to run all your tests in the pipeline for any new versions of the application to improve the quality What should you do?

A. 1. Install a Git hook to require developers to run unit tests before pushing the code to a central repository
2. Trigger Cloud Build to build the application container Deploy the application container to a testing environment, and run integration tests
3. If the integration tests are successful deploy the application container to your production environment.
and run acceptance tests
B. 1. Trigger Cloud Build to run unit tests when the code is pushed If all unit tests are successful, build and push the application container to a central registry.
2. Trigger Cloud Build to deploy the container to a testing environment, and run integration tests and acceptance tests
3. If all tests are successful the pipeline deploys the application to the production environment and runs smoke tests
C. 1. Install a Git hook to require developers to run unit tests before pushing the code to a central repository If all tests are successful build a container
2. Trigger Cloud Build to deploy the application container to a testing environment, and run integration tests and acceptance tests
3. If all tests are successful tag the code as production ready Trigger Cloud Build to build and deploy the application container to the production environment
D. 1. Trigger Cloud Build to build the application container and run unit tests with the container
2. If unit tests are successful, deploy the application container to a testing environment, and run integration tests
3. If the integration tests are successful the pipeline deploys the application container to the production environment After that, run acceptance tests

Answer: B

Explanation:
Explanation
The best option for building a CI/CD pipeline for a containerized application in Google Cloud is to trigger Cloud Build to run unit tests when the code is pushed, if all unit tests are successful, build and push the application container to a central registry, trigger Cloud Build to deploy the container to a testing environment, and run integration tests and acceptance tests, and if all tests are successful, the pipeline deploys the application to the production environment and runs smoke tests. This option follows the best practices for CI/CD pipelines, such as running tests at different stages of the pipeline, using a central registry for storing and managing containers, deploying to different environments, and using Cloud Build as a unified tool for building, testing, and deploying.

NEW QUESTION # 102
Your company is developing applications that are deployed on Google Kubernetes Engine (GKE). Each team manages a different application. You need to create the development and production environments for each team, while minimizing costs. Different teams should not be able to access other teams' environments. What should you do?

A. Create one GCP Project per team. In each project, create a cluster for Development and one for Production. Grant the teams IAM access to their respective clusters.
B. Create a Development and a Production GKE cluster in separate projects. In each cluster, create a Kubernetes namespace per team, and then configure Kubernetes Role-based access control (RBAC) so that each team can only access its own namespace.
C. Create a Development and a Production GKE cluster in separate projects. In each cluster, create a Kubernetes namespace per team, and then configure Identity Aware Proxy so that each team can only access its own namespace.
D. Create one GCP Project per team. In each project, create a cluster with a Kubernetes namespace for Development and one for Production. Grant the teams IAM access to their respective clusters.

Answer: B

NEW QUESTION # 103
Your company operates in a highly regulated domain that requires you to store all organization logs for seven years You want to minimize logging infrastructure complexity by using managed services You need to avoid any future loss of log capture or stored logs due to misconfiguration or human error What should you do?

A. Use Cloud Logging to configure an export sink at each project level to export all logs into a BigQuery dataset
B. Use Cloud Logging to configure an export sink at each project level to export all logs into Cloud Storage with a seven-year retention policy and Bucket Lock
C. Use Cloud Logging to configure an aggregated sink at the organization level to export all logs into a BigQuery dataset
D. Use Cloud Logging to configure an aggregated sink at the organization level to export all logs into Cloud Storage with a seven-year retention policy and Bucket Lock

Answer: D

NEW QUESTION # 104
Your company runs applications in Google Kubernetes Engine (GKE) that are deployed following a GitOps methodology.
Application developers frequently create cloud resources to support their applications. You want to give developers the ability to manage infrastructure as code, while ensuring that you follow Google-recommended practices. You need to ensure that infrastructure as code reconciles periodically to avoid configuration drift. What should you do?

A. Create a Job resource with a Terraform docker image to execute terraforrm plan and terraform apply commands.
B. Install and configure Config Connector in Google Kubernetes Engine (GKE).
C. Configure Cloud Build with a Terraform builder to execute plan and apply commands.
D. Create a Pod resource with a Terraform docker image to execute terraform plan and terraform apply commands.

Answer: B

NEW QUESTION # 105
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?

A. Enable logging on the firewall rule
B. Enable VPC Flow Logs on the subnet
C. Install the Ops Agent on the Compute Engine instances.
D. Enable Packet Mirroring on the VPC

Answer: B

Explanation:
The best option for configuring Cloud Logging to log each IP address that accesses the API by using the fewest number of steps is to enable logging on the firewall rule. A firewall rule is a rule that controls the traffic to and from your Compute Engine instances. You can enable logging on a firewall rule to capture information about the traffic that matches the rule, such as source and destination IP addresses, protocols, ports, and actions. You can use Cloud Logging to view and export the firewall logs to other destinations, such as BigQuery, for further analysis.

NEW QUESTION # 106
......

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