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Google Professional Cloud DevOps Engineer Certification Path

The Google Professional Cloud DevOps Engineer Certification is the highest level of certification mainly focusing on the Solution Architect Professional. There is no prerequisite for this exam but still, it would be best to follow some sequence to prove immense knowledge as a Google Professional Cloud DevOps Engineer.

Who should take the Google Professional Cloud DevOps Engineer exam

Individuals should pursue the Professional Cloud DevOps Engineer practice exam if they want to demonstrate their expertise and ability to design plan, and prototype a GCP DevOps, implement a GCP Virtual Private Cloud (VPC), implement DevOps security. It's perfect for DevOps engineers, systems administrators or operations team members, or simply any professional who wants in on this specific area of IT and cloud.

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Earning the Google Professional-Cloud-DevOps-Engineer Certification demonstrates that the candidate has the knowledge and skills to lead and manage cloud-native DevOps practices on the Google Cloud Platform. Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification is ideal for professionals who work as DevOps engineers, site reliability engineers, systems administrators, or software engineers who are responsible for deploying applications on the Google Cloud Platform. Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification is also suitable for IT managers who want to demonstrate their expertise in managing DevOps practices on the Google Cloud Platform.

Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q146-Q151):

NEW QUESTION # 146
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 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
B. Create one Google Cloud project per team In each project create a cluster for development and one forproduction 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 onlyaccess its own namespace
D. Create one Google Cloud project per team In each project create a cluster with a Kubernetes namespacefor development and one for production Grant the teams Identity and Access Management (1AM) access to their respective clusters.

Answer: A

NEW QUESTION # 147
Your team is building a service that performs compute-heavy processing on batches of data The data is processed faster based on the speed and number of CPUs on the machine These batches of data vary in size and may arrive at any time from multiple third-party sources You need to ensure that third parties are able to upload their data securely. You want to minimize costs while ensuring that the data is processed as quickly as possible What should you do?

A. * Provide a secure file transfer protocol (SFTP) server on a Compute Engine instance so that third parties can upload batches of data and provide appropriate credentials to the server
* Create a Cloud Function with a google.storage, object, finalize Cloud Storage trigger Write code so that the function can scale up a Compute Engine autoscaling managed instance group
* Use an image pre-loaded with the data processing software that terminates the instances when processing completes
B. * Provide a Cloud Storage bucket so that third parties can upload batches of data, and provide appropriate Identity and Access Management (1AM) access to the bucket
* Use Cloud Monitoring to detect new batches of data in the bucket and trigger a Cloud Function that processes the data
* Set a Cloud Function to use the largest CPU possible to minimize the runtime of the processing
C. * Provide a Cloud Storage bucket so that third parties can upload batches of data, and provide appropriate identity and Access Management (1AM) access to the bucket
* Create a Cloud Function with a google, storage, object .finalise Cloud Storage trigger Write code so that the function can scale up a Compute Engine autoscaling managed instance group
* Use an image pre-loaded with the data processing software that terminates the instances when processing completes
D. * Provide a Cloud Storage bucket so that third parties can upload batches of data, and provide appropriate Identity and Access Management (1AM) access to the bucket
* Use a standard Google Kubernetes Engine (GKE) cluster and maintain two services one that processes the batches of data and one that monitors Cloud Storage for new batches of data
* Stop the processing service when there are no batches of data to process

Answer: C

Explanation:
Explanation
The best option for ensuring that third parties are able to upload their data securely and minimizing costs while ensuring that the data is processed as quickly as possible is to provide a Cloud Storage bucket so that third parties can upload batches of data, and provide appropriate Identity and Access Management (IAM) access to the bucket; create a Cloud Function with a google.storage.object.finalize Cloud Storage trigger; write code so that the function can scale up a Compute Engine autoscaling managed instance group; use an image pre-loaded with the data processing software that terminates the instances when processing completes. A Cloud Storage bucket is a resource that allows you to store and access data in Google Cloud. You can provide a Cloud Storage bucket so that third parties can upload batches of data securely and conveniently. You can also provide appropriate IAM access to the bucket by using roles and policies to control who can read or write data to the bucket. A Cloud Function is a serverless function that executes code in response to an event, such as a change in a Cloud Storage bucket. A google.storage.object.finalize trigger is a type of trigger that fires when a new object is created or an existing object is overwritten in a Cloud Storage bucket. You can create a Cloud Function with a google.storage.object.finalize trigger so that the function runs whenever a new batch of data is uploaded to the bucket. You can write code so that the function can scale up a Compute Engine autoscaling managed instance group, which is a group of VM instances that automatically adjusts its size based on load or custom metrics. You can use an image pre-loaded with the data processing software that terminates the instances when processing completes, which means that the instances only run when there is data to process and stop when they are done. This way, you can minimize costs while ensuring that the data is processed as quickly as possible.

NEW QUESTION # 148
You need to create a Cloud Monitoring SLO for a service that will be published soon. You want to verify that requests to the service will be addressed in fewer than 300 ms at least 90% Of the time per calendar month. You need to identify the metric and evaluation method to use. What should you do?

A. Select an availability metric for a request-based method of evaluation.
B. Select a latency metric for a window-based method of evaluation.
C. Select an availability metric for a window-based method Of evaluation.
D. Select a latency metric for a request-based method of evaluation.

Answer: D

Explanation:
The correct answer is
A, Select a latency metric for a request-based method of evaluation.
A latency metric measures how responsive your service is to users. For example, you can use the cloud.googleapis.com/http/server/response_latencies metric to measure the latency of HTTP requests to your service1. A request-based method of evaluation counts the number of successful requests that meet a certain criterion, such as being below a latency threshold, and compares it to the number of all requests. For example, you can define an SLI as the ratio of requests with latency below 300 ms to all requests2. A request-based method of evaluation is suitable for measuring performance over time, such as per calendar month. You can set an SLO for the SLI to be at least 90%, which means that you expect 90% of the requests to have latency below 300 ms in a month3.
Reference:
Creating an SLO | Operations Suite | Google Cloud, Choosing a metric, Latency metric. Concepts in service monitoring | Operations Suite | Google Cloud, Service-level indicators, Request-based SLIs. Learn how to set SLOs - SRE tips | Google Cloud Blog, Setting SLOs.

NEW QUESTION # 149
Your organization has a containerized web application that runs on-premises As part of the migration plan to Google Cloud you need to select a deployment strategy and platform that meets the following acceptance criteria
1 The platform must be able to direct traffic from Android devices to an Android-specific microservice
2 The platform must allow for arbitrary percentage-based traffic splitting
3 The deployment strategy must allow for continuous testing of multiple versions of any microservice What should you do?

A. Deploy the canary release of the application to Cloud Run Use traffic splitting to direct 10% of user traffic to the canary release based on the revision tag
B. Deploy the canary release of the application to Compute Engine Use Anthos Service Mesh with Compute Engine to direct 10% of user traffic to the canary release by configuring the virtual service.
C. Deploy the canary release to Google Kubernetes Engine with Anthos Sen/ice Mesh Use traffic splitting to direct 10% of user traffic to the new version based on the user-agent header configured in the virtual service
D. Deploy the canary release of the application to App Engine Use traffic splitting to direct a subset of user traffic to the new version based on the IP address

Answer: C

Explanation:
Explanation
The best option for deploying a containerized web application to Google Cloud with the given acceptance criteria is to use Google Kubernetes Engine (GKE) with Anthos Service Mesh. GKE is a managed service for running Kubernetes clusters on Google Cloud, and Anthos Service Mesh is a service mesh that provides observability, traffic management, and security features for microservices. With Anthos Service Mesh, you can use traffic splitting to direct traffic from Android devices to an Android-specific microservice by configuring the user-agent header in the virtual service. You can also use traffic splitting to direct arbitrary percentage-based traffic to different versions of any microservice for continuous testing. For example, you can use a canary release strategy to direct 10% of user traffic to a new version of a microservice and monitor its performance and reliability.

NEW QUESTION # 150
You are using Terraform to manage infrastructure as code within a Cl/CD pipeline You notice that multiple copies of the entire infrastructure stack exist in your Google Cloud project, and a new copy is created each time a change to the existing infrastructure is made You need to optimize your cloud spend by ensuring that only a single instance of your infrastructure stack exists at a time. You want to follow Google-recommended practices What should you do?

A. Confirm that the pipeline is storing and retrieving the terraform. if state file from Cloud Storage with the Terraform gcs backend
B. Verify that the pipeline is storing and retrieving the terrafom.tfstat* file from a source control
C. Update the pipeline to remove any existing infrastructure before you apply the latest configuration
D. Create a new pipeline to delete old infrastructure stacks when they are no longer needed

Answer: A

Explanation:
The best option for optimizing your cloud spend by ensuring that only a single instance of your infrastructure stack exists at a time is to confirm that the pipeline is storing and retrieving the terraform.tfstate file from Cloud Storage with the Terraform gcs backend. The terraform.tfstate file is a file that Terraform uses to store the current state of your infrastructure. The Terraform gcs backend is a backend type that allows you to store the terraform.tfstate file in a Cloud Storage bucket. By using the Terraform gcs backend, you can ensure that your pipeline has access to the latest state of your infrastructure and avoid creating multiple copies of the entire infrastructure stack.

NEW QUESTION # 151
......

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