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

Section	Objectives
Implement security and compliance	- Compliance and governance 1. Audit logging and policy enforcement - Secure CI/CD pipelines 1. IAM and least privilege access 2. Secret and credential management
Optimize performance and continuous delivery	- Monitoring and observability 1. Performance tuning and feedback loops 2. Cloud Monitoring and Logging - System performance optimization 1. Scaling strategies and load handling
Develop and implement CI/CD pipelines	- Build and manage CI/CD pipelines using Google Cloud tools 1. Artifact repository management 2. Cloud Build pipeline design - Automate build, test, and deployment processes 1. Deployment automation strategies 2. Release management practices
Implement site reliability engineering (SRE) practices	- Incident management and postmortems 1. Alerting and on-call practices - Define and manage SLI, SLO, and SLA 1. Error budgets and monitoring strategies

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q17-Q22):

NEW QUESTION # 17
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. Pre-provision the additional compute power that was used last season, and expect growth.
B. Load test the application to profile its performance for scaling.
C. Create a Terraform configuration for the application's underlying infrastructure to quickly deploy to additional regions.
D. Migrate the application to Cloud Run, and use autoscaling.

Answer: B

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.
Reference:
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 # 18
You are running an experiment to see whether your users like a new feature of a web application. Shortly after deploying the feature as a canary release, you receive a spike in the number of 500 errors sent to users, and your monitoring reports show increased latency. You want to quickly minimize the negative impact on users.
What should you do first?

A. Start monitoring latency, traffic, errors, and saturation.
B. Roll back the experimental canary release.
C. Trace the origin of 500 errors and the root cause of increased latency.
D. Record data for the postmortem document of the incident.

Answer: B

NEW QUESTION # 19
You are building and deploying a microservice on Cloud Run for your organization Your service is used by many applications internally You are deploying a new release, and you need to test the new version extensively in the staging and production environments You must minimize user and developer impact. What should you do?

A. Deploy the new version of the service to the staging environment with a new-release tag without serving traffic Test the new-release version If the test passes; gradually roll out this tagged version Repeat for the production environment
B. Deploy the new version of the service to the staging environment Split the traffic, and allow 1 % of traffic through to the latest version Test the latest version If the test passes gradually roll out the latest version to the staging and production environments
C. Deploy the new version of the service to the staging environment Split the traffic, and allow 50% of traffic through to the latest version Test the latest version If the test passes, send all traffic to the latest version Repeat for the production environment
D. Deploy a new environment with the green tag to use as the staging environment Deploy the new version of the service to the green environment and test the new version If the tests pass, send all traffic to the green environment and delete the existing staging environment Repeat for the production environment

Answer: A

Explanation:
Explanation
The best option for deploying a new release of your microservice on Cloud Run and testing it extensively in the staging and production environments with minimal user and developer impact is to deploy the new version of the service to the staging environment with a new-release tag without serving traffic, test the new-release version, and if the test passes, gradually roll out this tagged version. A tag is a label that you can assign to a revision of your service on Cloud Run. You can use tags to create different versions of your service without affecting traffic. You can also use tags to gradually roll out traffic to a new version of your service by using traffic splitting. This way, you can test your new release extensively in both environments and minimize user and developer impact.

NEW QUESTION # 20
Your team is designing a new application for deployment both inside and outside Google Cloud Platform (GCP). You need to collect detailed metrics such as system resource utilization. You want to use centralized GCP services while minimizing the amount of work required to set up this collection system. What should you do?

A. Install an Application Performance Monitoring (APM) tool in both locations, and configure an export to a central data storage location for analysis.
B. Import the Stackdriver Debugger package, and configure the application to emit debug messages with timing information.
C. Import the Stackdriver Profiler package, and configure it to relay function timing data to Stackdriver for further analysis.
D. Instrument the code using a timing library, and publish the metrics via a health check endpoint that is scraped by Stackdriver.

Answer: C

NEW QUESTION # 21
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. 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
B. 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
C. 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
D. 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

Answer: A

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 # 22
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