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Google Professional-Cloud-DevOps-Engineer Sample Questions – Free Practice Test & Real Exam Prep
Question #1
The new version of your containerized application has been tested and is ready to be deployed to
production on Google Kubernetes Engine (GKE) You could not fully load-test the new version in your
pre-production environment and you need to ensure that the application does not have performance
problems after deployment Your deployment must be automated What should you do?
A. Deploy the application through a continuous delivery pipeline by using canary deployments Use
Cloud Monitoring to look for performance issues, and ramp up traffic as supported by the metrics
B. Deploy the application through a continuous delivery pipeline by using blue/green deployments
Migrate traffic to the new version of the application and use Cloud Monitoring to look for
performance issues
C. Deploy the application by using kubectl and use Config Connector to slowly ramp up traffic
between versions. Use Cloud Monitoring to look for performance issues
D. Deploy the application by using kubectl and set the spec. updatestrategy. type field to
RollingUpdate Use Cloud Monitoring to look for performance issues, and run the kubectl rollback
command if there are any issues.
Answer: A Explanation:
The best option for deploying a new version of your containerized application to production on GKE
and ensuring that the application does not have performance problems after deployment is to
deploy the application through a continuous delivery pipeline by using canary deployments, use
Cloud Monitoring to look for performance issues, and ramp up traffic as supported by the metrics. A
canary deployment is a deployment strategy that involves releasing a new version of an application
to a subset of users or servers and monitoring its performance and reliability. This way, you can test
the new version in the production environment with real traffic and load, and gradually increase the
traffic as the metrics indicate. You can use Cloud Monitoring to collect and analyze metrics from your
application and GKE cluster, such as latency, error rate, CPU utilization, and memory usage. You can
also use Cloud Monitoring to set up alerts and dashboards to track the performance of your
application.
Question #2
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 you minimize costs Different teams should not be able to access
other teams environments You want to follow Google-recommended practices What should you do?
A. Create one Google Cloud project per team In each project create a cluster for development and
one for
production Grant the teams Identity and Access Management (1AM) access to their respective
clusters
B. Create one Google Cloud project per team In each project create a cluster with a Kubernetes
namespace
for development and one for production Grant the teams Identity and Access Management (1AM)
access to their respective clusters
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 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
Answer: D Explanation:
The best option for creating the development and production environments for each team while
minimizing costs and ensuring isolation is to 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.
This option allows you to use fewer clusters and projects than creating one project or cluster per
team, which reduces costs and complexity. It also allows you to isolate each teams environment by
using namespaces and RBAC, which prevents teams from accessing other teams environments.
Question #3
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. 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. 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
Answer: D 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.
Question #4
Your company runs services by using multiple globally distributed Google Kubernetes Engine (GKE)
clusters Your operations team has set up workload monitoring that uses Prometheus-based tooling
for metrics alerts: and generating dashboards This setup does not provide a method to view metrics
globally across all clusters You need to implement a scalable solution to support global Prometheus
querying and minimize management overhead What should you do?
A. Configure Prometheus cross-service federation for centralized data access
B. Configure workload metrics within Cloud Operations for GKE
C. Configure Prometheus hierarchical federation for centralized data access
D. Configure Google Cloud Managed Service for Prometheus
Answer: D Explanation:
The best option for implementing a scalable solution to support global Prometheus querying and
minimize management overhead is to use Google Cloud Managed Service for Prometheus. Google
Cloud Managed Service for Prometheus is a fully managed service that allows you to collect, query,
and visualize metrics from your GKE clusters using Prometheus-based tooling. You can use Google
Cloud Managed Service for Prometheus to query metrics across multiple clusters and regions using a
global view. You can also use Google Cloud Managed Service for Prometheus to integrate with other
Google Cloud services, such as Cloud Monitoring, Cloud Logging, and BigQuery. By using Google
Cloud Managed Service for Prometheus, you can avoid managing and scaling your own Prometheus
servers and focus on your application performance.
Question #5
You deployed an application into a large Standard Google Kubernetes Engine (GKE) cluster. Theapplication is stateless and multiple pods run at the same time. Your application receivesinconsistent traffic. You need to ensure that the user experience remains consistent regardless ofchanges in traffic. and that the resource usage of the cluster is optimized.What should you do?
A. Configure a cron job to scale the deployment on a schedule.
B. Configure a Horizontal Pod Autoscaler.
C. Configure a Vertical Pod Autoscaler.
D. Configure cluster autoscaling on the node pool.
Answer: B
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