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Google Professional-Cloud-DevOps-Engineer Exam Questions

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam

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Google Professional-Cloud-DevOps-Engineer Sample Questions – Free Practice Test & Real Exam Prep

Question #1

You have an application that runs on Cloud Run. You want to use live production traffic to test a newversion of the application while you let the quality assurance team perform manual testing. You wantto limit the potential impact of any issues while testing the new version, and you must be able to rollback to a previous version of the application if needed. How should you deploy the new version?Choose 2 answers

  • A. Deploy the application as a new Cloud Run service.
  • B. Deploy a new Cloud Run revision with a tag and use the ”no-traffic option.
  • C. Deploy a new Cloud Run revision without a tag and use the ”no-traffic option.
  • D. Deploy the new application version and use the ”no-traffic option Route production traffic to therevision's URL
  • E. Deploy the new application version and split traffic to the new version.
Answer: B, E

Question #2

You need to introduce postmortems into your organization. You want to ensure that the postmortemprocess is well received. What should you do?Choose 2 answers

  • A. Create a designated team that is responsible for conducting all postmortems.
  • B. Encourage new employees to conduct postmortems to learn through practice.
  • C. Ensure that writing effective postmortems is a rewarded and celebrated practice.
  • D. Encourage your senior leadership to acknowledge and participate in postmortems.
  • E. Provide your organization with a forum to critique previous postmortems.
Answer: C, D

Question #3

You need to introduce postmortems into your organization during the holiday shopping season. Youare expecting your web application to receive a large volume of traffic in a short period. You need toprepare your application for potential failures during the event What should you do?Choose 2 answers

  • A. Monitor latency of your services for average percentile latency.
  • B. Review your increased capacity requirements and plan for the required quota management.
  • C. Create alerts in Cloud Monitoring for all common failures that your application experiences.
  • D. Ensure that relevant system metrics are being captured with Cloud Monitoring and create alerts atlevels of interest
  • E. Configure Anthos Service Mesh on the application to identify issues on the topology map.
Answer: B, D

Question #4

Your company operates in a highly regulated domain. Your security team requires that only trustedcontainer images can be deployed to Google Kubernetes Engine (GKE). You need to implement asolution that meets the requirements of the security team, while minimizing management overhead.What should you do?

  • A. Grant the roles/artifactregistry. writer role to the Cloud Build service account. Confirm that noemployee has Artifact Registry write permission.
  • B. Use Cloud Run to write and deploy a custom validator Enable an Eventarc trigger to performvalidations when new images are uploaded.
  • C. Configure Kritis to run in your GKE clusters to enforce deploy-time security policies.
  • D. Configure Binary Authorization in your GKE clusters to enforce deploy-time security policies
Answer: D

Question #5

Your organization stores all application logs from multiple Google Cloud projects in a central CloudLogging project. Your security team wants to enforce a rule that each project team can only viewtheir respective logs, and only the operations team can view all the logs. You need to design asolution that meets the security team's requirements, while minimizing costs. What should you do?

  • A. Export logs to BigQuery tables for each project team. Grant project teams access to their tables.Grant logs writer access to the operations team in the central logging project.
  • B. Create log views for each project team, and only show each project team their application logs.Grant the operations team access to the _ Al Il-jogs View in the central logging project.
  • C. Grant each project team access to the project _ Default view in the central logging project. Grantlogging viewer access to the operations team in the central logging project.
  • D. Create Identity and Access Management (IAM) roles for each project team and restrict access tothe _ Default log view in their individual Google Cloud project. Grant viewer access to the operationsteam in the central logging project.
Answer: B
Explanation:
Create log views for each project team, and only show each project team their application logs. Grant
the operations team access to the _AllLogs View in the central logging project1.
This approach aligns with the Google Clouds recommended methodologies for Professional Cloud
DevOps Engineers1. Log views allow you to create and manage access control at a finer granularity
for your logs. By creating a separate log view for each project team, you can ensure that they only
have access to their respective logs. The operations team, on the other hand, can be granted access
to the _AllLogs view in the central logging project, allowing them to view all logs as required.
This solution not only meets the security teams requirements but also minimizes costs as it
leverages built-in features of Google Clouds logging and does not require exporting logs to another
service like BigQuery (as suggested in option A), which could incur additional costs1.

Question #6

You are configuring a Cl pipeline. The build step for your Cl pipeline integration testing requiresaccess to APIs inside your private VPC network. Your security team requires that you do not exposeAPI traffic publicly. You need to implement a solution that minimizes management overhead. Whatshould you do?

  • A. Use Cloud Build private pools to connect to the private VPC.
  • B. Use Spinnaker for Google Cloud to connect to the private VPC.
  • C. Use Cloud Build as a pipeline runner. Configure Internal HTTP(S) Load Balancing for API access.
  • D. Use Cloud Build as a pipeline runner. Configure External HTTP(S) Load Balancing with a GoogleCloud Armor policy for API access.
Answer: A
Explanation:
Cloud Build is a service that executes your builds on Google Cloud Platform infrastructure1. Cloud
Build can be used as a pipeline runner for your CI pipeline, which is a process that automates the
integration and testing of your code2. Cloud Build private pools are private, dedicated pools of
workers that offer greater customization over the build environment, including the ability to access
resources in a private VPC network3. A VPC network is a virtual network that provides connectivity
for your Google Cloud resources and services. By using Cloud Build private pools, you can implement
a solution that minimizes management overhead, as Cloud Build private pools are hosted and fullymanaged
by Cloud Build and scale up and down to zero, with no infrastructure to set up, upgrade, or
scale3. You can also implement a solution that meets your security requirement, as Cloud Build
private pools use network peering to connect into your private VPC network and do not expose API
traffic publicly
Question #7

Your company recently migrated to Google Cloud. You need to design a fast, reliable, and repeatablesolution for your company to provision new projects and basic resources in Google Cloud. Whatshould you do?

  • A. Use the Google Cloud console to create projects.
  • B. Write a script by using the gcloud CLI that passes the appropriate parameters from the request.Save the script in a Git repository.
  • C. Write a Terraform module and save it in your source control repository. Copy and run the applycommand to create the new project.
  • D. Use the Terraform repositories from the Cloud Foundation Toolkit. Apply the code withappropriate parameters to create the Google Cloud project and related resources.
Answer: D
Explanation:
Terraform is an open-source tool that allows you to define and provision infrastructure as
code1. Terraform can be used to create and manage Google Cloud resources, such as projects,
networks, and services2. The Cloud Foundation Toolkit is a set of open-source Terraform modules
and tools that provide best practices and guidance for deploying Google Cloud infrastructure3. The
Cloud Foundation Toolkit includes Terraform repositories for creating Google Cloud projects and
related resources, such as IAM policies, APIs, service accounts, and billing4. By using the Terraform
repositories from the Cloud Foundation Toolkit, you can design a fast, reliable, and repeatable
solution for your company to provision new projects and basic resources in Google Cloud. You can
also customize the Terraform code to suit your specific needs and preferences.
Question #8

You need to enforce several constraint templates across your Google Kubernetes Engine (GKE)clusters. The constraints include policy parameters, such as restricting the Kubernetes API. You mustensure that the policy parameters are stored in a GitHub repository and automatically applied whenchanges occur. What should you do?

  • A. Set up a GitHub action to trigger Cloud Build when there is a parameter change. In Cloud Build,run a gcloud CLI command to apply the change.
  • B. When there is a change in GitHub, use a web hook to send a request to Anthos Service Mesh, andapply the change
  • C. Configure Anthos Config Management with the GitHub repository. When there is a change in therepository, use Anthos Config Management to apply the change.
  • D. Configure Config Connector with the GitHub repository. When there is a change in the repository,use Config Connector to apply the change.
Answer: C
Explanation:
The correct answer is C. Configure Anthos Config Management with the GitHub repository. When
there is a change in the repository, use Anthos Config Management to apply the change.
According to the web search results, Anthos Config Management is a service that lets you manage
the configuration of your Google Kubernetes Engine (GKE) clusters from a single source of truth, such
as a GitHub repository1. Anthos Config Management can enforce several constraint templates across
your GKE clusters by using Policy Controller, which is a feature that integrates the Open Policy Agent
(OPA) Constraint Framework into Anthos Config Management2. Policy Controller can apply
constraints that include policy parameters, such as restricting the Kubernetes API3. To use Anthos
Config Management and Policy Controller, you need to configure them with your GitHub repository
and enable the sync mode4. When there is a change in the repository, Anthos Config Management
will automatically sync and apply the change to your GKE clusters5.
The other options are incorrect because they do not use Anthos Config Management and Policy
Controller. Option A is incorrect because it uses a GitHub action to trigger Cloud Build, which is a
service that executes your builds on Google Cloud Platform infrastructure6. Cloud Build can run a
gcloud CLI command to apply the change, but it does not use Anthos Config Management or Policy
Controller. Option B is incorrect because it uses a web hook to send a request to Anthos Service
Mesh, which is a service that provides a uniform way to connect, secure, monitor, and manage
microservices on GKE clusters7. Anthos Service Mesh can apply the change, but it does not use
Anthos Config Management or Policy Controller. Option D is incorrect because it uses Config
Connector, which is a service that lets you manage Google Cloud resources through Kubernetes
configuration. Config Connector can apply the change, but it does not use Anthos Config
Management or Policy Controller.
Reference:
Anthos Config Management documentation, Overview. Policy Controller, Policy Controller.
Constraint template library, Constraint template library. Installing Anthos Config Management,
Installing Anthos Config Management. Syncing configurations, Syncing configurations. Cloud Build
documentation, Overview. Anthos Service Mesh documentation, Overview. [Config Connector
documentation], Overview
Question #9

You need to define SLOs for a high-traffic web application. Customers are currently happy with theapplication performance and availability. Based on current measurement, the 90th percentile Oflatency is 160 ms and the 95thpercentile of latency is 300 ms over a 28-day window. What latency SLO should you publish?

  • A. 90th percentile - 150 ms95th percentile - 290 ms
  • B. 90th percentile - 160 ms95th percentile - 300 ms
  • C. 90th percentile - 190 ms95th percentile - 330 ms
  • D. 90th percentile - 300 ms95th percentile - 450 ms
Answer: B
Explanation:
a latency SLO is a service level objective that specifies a target level of responsiveness for a web
application1. A latency SLO can be expressed as a percentile of latency over a time window, such as
the 90th percentile of latency over 28 days2. A percentile of latency is the maximum amount of time
that a given percentage of requests take to complete. For example, the 90th percentile of latency is
the maximum amount of time that 90% of requests take to complete3.
To define a latency SLO, you need to consider the following factors24:
The expectations and satisfaction of your customers. You want to set a latency SLO that reflects the
level of performance that your customers are happy with and willing to pay for.
The current and historical measurements of your latency. You want to set a latency SLO that is based
on data and realistic for your web application.
The trade-offs and costs of improving your latency. You want to set a latency SLO that balances the
benefits of faster response times with the costs of engineering work, infrastructure, and complexity.
Based on these factors, the best option for defining a latency SLO for your web application is option
B. Option B sets the latency SLO to match the current measurement of your latency, which means
that you are meeting the expectations and satisfaction of your customers. Option B also sets a
realistic and achievable target for your web application, which means that you do not need to invest
extra resources or effort to improve your latency. Option B also aligns with the best practice of
setting conservative SLOs, which means that you have some buffer or margin for error in case your
latency fluctuates or degrades5.

Question #10

Your company processes IOT data at scale by using Pub/Sub, App Engine standard environment, andan application written in GO. You noticed that the performance inconsistently degrades at peak load.You could not reproduce this issue on your workstation. You need to continuously monitor theapplication in production to identify slow paths in the code. You want to minimize performanceimpact and management overhead. What should you do?

  • A. Install a continuous profiling tool into Compute Engine. Configure the application to send profilingdata to the tool.
  • B. Periodically run the go tool pprof command against the application instance. Analyze the results byusing flame graphs.
  • C. Configure Cloud Profiler, and initialize the [email protected]/go/profiler library in the application.
  • D. Use Cloud Monitoring to assess the App Engine CPU utilization metric.
Answer: C
Explanation:
The correct answer is C. Configure Cloud Profiler, and initialize the cloud.google.com/go/profiler
library in the application.
According to the Google Cloud documentation, Cloud Profiler is a statistical, low-overhead profiler
that continuously gathers CPU usage and memory-allocation information from your production
applications1. Cloud Profiler can help you identify slow paths in your code and optimize the
performance of your applications. Cloud Profiler supports applications written in Go that run on App
Engine standard environment2. To use Cloud Profiler, you need to configure it in your Google Cloud
project and initialize the cloud.google.com/go/profiler library in your application code3. You can then
use the Cloud Profiler interface to analyze the profiling data and visualize the results by using flame
graphs4. Cloud Profiler has minimal performance impact and management overhead, as it only
samples a small fraction of the application activity and does not require any additional infrastructure
or agents.
The other options are incorrect because they do not meet the requirements of minimizing
performance impact and management overhead. Option A is incorrect because it requires installing a
continuous profiling tool into Compute Engine, which is an additional infrastructure that needs to be
managed and maintained. Option B is incorrect because it requires periodically running the go tool
pprof command against the application instance, which is a manual and disruptive process that can
affect the application performance. Option D is incorrect because it only uses Cloud Monitoring to
assess the App Engine CPU utilization metric, which is not enough to identify slow paths in the code
or optimize the application performance.
Reference:
Cloud Profiler documentation, Overview. Profiling Go applications, Supported environments.
Profiling Go applications, Using Cloud Profiler. Analyzing data, Analyzing data
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