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Professional-Cloud-DevOps-Engineer Exam Vce Format & Latest Professional-Cloud-DevOps-Engineer Test Guide

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Google Professional-Cloud-DevOps-Engineer certification exam is a comprehensive exam that covers a wide range of topics related to DevOps practices and GCP services. Professional-Cloud-DevOps-Engineer exam tests the candidate's knowledge and understanding of DevOps methodologies, including continuous integration and delivery, automation, and monitoring. It also assesses their ability to implement GCP services such as Compute Engine, App Engine, Kubernetes Engine, and Cloud Functions, among others.

Google Professional-Cloud-DevOps-Engineer exam is a certification test designed to assess the knowledge and skills of individuals in the field of Cloud DevOps engineering. Professional-Cloud-DevOps-Engineer exam is one of the most sought-after certifications for IT professionals who want to prove their proficiency in designing, building, and managing applications on the Google Cloud Platform.

Google Professional-Cloud-DevOps-Engineer Certification is highly valued in the industry as it validates the skills and knowledge of professionals who are working in the field of cloud DevOps engineering. Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification program is designed to help professionals enhance their career prospects and demonstrate their expertise in cloud computing and DevOps principles.

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q33-Q38):

NEW QUESTION # 33
Your application images are built and pushed to Google Container Registry (GCR). You want to build an automated pipeline that deploys the application when the image is updated while minimizing the development effort. What should you do?

A. Use a custom builder in Cloud Build to trigger a Jenkins pipeline.
B. Use Cloud Pub/Sub to trigger a Spinnaker pipeline.
C. Use Cloud Build to trigger a Spinnaker pipeline.
D. Use Cloud Pub/Sub to trigger a custom deployment service running in Google Kubernetes Engine (GKE).

Answer: B

Explanation:
Explanation
https://cloud.google.com/architecture/continuous-delivery-toolchain-spinnaker-cloud
https://spinnaker.io/guides/user/pipeline/triggers/pubsub/
The most efficient way to build an automated pipeline that deploys the application when the image is updated is to use Cloud Pub/Sub to trigger a Spinnaker pipeline. This way, you can leverage the built-in integration between GCR and Cloud Pub/Sub, and use Spinnaker as a continuous delivery platform for deploying your application .

NEW QUESTION # 34
Your team has recently deployed an NGINX-based application into Google Kubernetes Engine (GKE) and has exposed it to the public via an HTTP Google Cloud Load Balancer (GCLB) ingress. You want to scale the deployment of the application's frontend using an appropriate Service Level Indicator (SLI). What should you do?

A. Install the Stackdriver custom metrics adapter and configure a horizontal pod autoscaler to use the number of requests provided by the GCLB.
B. Configure the horizontal pod autoscaler to use the average response time from the Liveness and Readiness probes.
C. Expose the NGINX stats endpoint and configure the horizontal pod autoscaler to use the request metrics exposed by the NGINX deployment.
D. Configure the vertical pod autoscaler in GKE and enable the cluster autoscaler to scale the cluster as pods expand.

Answer: D

NEW QUESTION # 35
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 with a Kubernetes namespace for development and one for production Grant the teams Identity and Access Management (1AM) 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 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
D. 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

Answer: B

Explanation:
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 team's environment by using namespaces and RBAC, which prevents teams from accessing other teams' environments.

NEW QUESTION # 36
You are implementing a CI'CD pipeline for your application in your company s multi-cloud environment Your application is deployed by using custom Compute Engine images and the equivalent in other cloud providers You need to implement a solution that will enable you to build and deploy the images to your current environment and is adaptable to future changes Which solution stack should you use'?

A. Cloud Build with kpt
B. Cloud Build with Google Cloud Deploy
C. Google Kubernetes Engine with Google Cloud Deploy
D. Cloud Build with Packer

Answer: B

Explanation:
Explanation
Cloud Build is a fully managed continuous integration and continuous delivery (CI/CD) service that helps you automate your builds, tests, and deployments. Google Cloud Deploy is a service that automates the deployment of your applications to Google Kubernetes Engine (GKE).
Together, Cloud Build and Google Cloud Deploy can be used to build and deploy your application's custom Compute Engine images to your current environment and to other cloud providers in the future.
Here are the steps involved in using Cloud Build and Google Cloud Deploy to implement a CI/CD pipeline for your application:
Create a Cloud Build trigger that fires whenever a change is made to your application's code.
In the Cloud Build trigger, configure Cloud Build to build your application's Docker image.
Create a Google Cloud Deploy configuration file that specifies how to deploy your application's Docker image to GKE.
In Google Cloud Deploy, create a deployment that uses your configuration file.
Once you have created the Cloud Build trigger and Google Cloud Deploy configuration file, any changes made to your application's code will trigger Cloud Build to build a new Docker image. Google Cloud Deploy will then deploy the new Docker image to GKE.
This solution stack is adaptable to future changes because it uses a cloud-agnostic approach. Cloud Build can be used to build Docker images for any cloud provider, and Google Cloud Deploy can be used to deploy Docker images to any Kubernetes cluster.
The other solution stacks are not as adaptable to future changes. For example, solution stack A (Cloud Build with Packer) is limited to building Docker images for Compute Engine. Solution stack C (Google Kubernetes Engine with Google Cloud Deploy) is limited to deploying Docker images to GKE. Solution stack D (Cloud Build with kpt) is a newer solution that is not yet as mature as Cloud Build and Google Cloud Deploy.
Overall, the best solution stack for implementing a CI/CD pipeline for your application in a multi-cloud environment is Cloud Build with Google Cloud Deploy. This solution stack is fully managed, cloud-agnostic, and adaptable to future changes.

NEW QUESTION # 37
You are running an application on Compute Engine and collecting logs through Stackdriver. You discover that some personally identifiable information (Pll) is leaking into certain log entry fields. All Pll entries begin with the text userinfo. You want to capture these log entries in a secure location for later review and prevent them from leaking to Stackdriver Logging. What should you do?

A. Create an advanced log filter matching userinfo, configure a log export in the Stackdriver console with Cloud Storage as a sink, and then configure a tog exclusion with userinfo as a filter.
B. Use a Fluentd filter plugin with the Stackdriver Agent to remove log entries containing userinfo, create an advanced log filter matching userinfo, and then configure a log export in the Stackdriver console with Cloud Storage as a sink.
C. Create a basic log filter matching userinfo, and then configure a log export in the Stackdriver console with Cloud Storage as a sink.
D. Use a Fluentd filter plugin with the Stackdriver Agent to remove log entries containing userinfo, and then copy the entries to a Cloud Storage bucket.

Answer: C

NEW QUESTION # 38
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