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The Google Professional-Cloud-DevOps-Engineer exam focuses on various aspects of DevOps, including continuous delivery, infrastructure as code, monitoring and logging, and security. Candidates who pass the exam are recognized as experts in implementing and managing DevOps workflows on Google Cloud Platform. They are also able to design and implement highly scalable and reliable cloud-based applications.

Google Cloud Certified - Professional Cloud DevOps Engineer is a certification exam that validates the skills of the candidates in the field of cloud computing and DevOps. Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification exam is designed for professionals who have a good understanding of DevOps practices and tools, and can implement them in the Google Cloud Platform (GCP) environment. Professional-Cloud-DevOps-Engineer Exam is intended to test the candidate's ability to design, develop, and manage a reliable, scalable, and secure cloud-based infrastructure.

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Google Professional-Cloud-DevOps-Engineer Exam is a performance-based exam that evaluates the candidate's practical skills in the real-world scenario. Professional-Cloud-DevOps-Engineer exam is conducted online, and the candidates are required to complete a set of tasks within a given time frame. Professional-Cloud-DevOps-Engineer exam is divided into two parts - a scenario-based section and a hands-on section. The scenario-based section tests the candidate's ability to analyze and solve complex problems, while the hands-on section evaluates the candidate's skills in implementing and managing GCP solutions.

Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q52-Q57):

NEW QUESTION # 52
You need to deploy a new service to production. The service needs to automatically scale using a Managed Instance Group (MIG) and should be deployed over multiple regions. The service needs a large number of resources for each instance and you need to plan for capacity. What should you do?

Answer: B


NEW QUESTION # 53
You are designing a deployment technique for your applications on Google Cloud. As part Of your deployment planning, you want to use live traffic to gather performance metrics for new versions Ofyour applications. You need to test against the full production load before your applications are launched. What should you do?

Answer: A

Explanation:
The correct answer is B. Use shadow testing with continuous deployment.
Shadow testing is a deployment technique that involves routing a copy of the live traffic to a new version of the application, without affecting the production environment. This way, you can gather performance metrics and compare them with the current version, without exposing the new version to the users. Shadow testing can help you test against the full production load and identify any issues or bottlenecks before launching the new version. You can use continuous deployment to automate the process of deploying the new version after it passes the shadow testing.


NEW QUESTION # 54
You are configuring a Cl pipeline. The build step for your Cl pipeline integration testing requires access to APIs inside your private VPC network. Your security team requires that you do not expose API traffic publicly. You need to implement a solution that minimizes management overhead. What should you do?

Answer: D

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 fully-managed 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.


NEW QUESTION # 55
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?

Answer: A


NEW QUESTION # 56
Your company runs an ecommerce website built with JVM-based applications and microservice architecture in Google Kubernetes Engine (GKE) The application load increases during the day and decreases during the night Your operations team has configured the application to run enough Pods to handle the evening peak load You want to automate scaling by only running enough Pods and nodes for the load What should you do?

Answer: D

Explanation:
The best option for automating scaling by only running enough Pods and nodes for the load is to configure the Horizontal Pod Autoscaler and enable the cluster autoscaler. The Horizontal Pod Autoscaler is a feature that automatically adjusts the number of Pods in a deployment or replica set based on observed CPU utilization or custom metrics. The cluster autoscaler is a feature that automatically adjusts the size of a node pool based on the demand for node capacity. By using both features together, you can ensure that your application runs enough Pods to handle the load, and that your cluster runs enough nodes to host the Pods. This way, you can optimize your resource utilization and cost efficiency.


NEW QUESTION # 57
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