Ask Question
Professional Cloud DevOps Engineer: Professional Cloud DevOps Engineer
Exam Questions:
166
.png)
2.370 Learners
The Professional Cloud DevOps Engineer exam is crucial for IT professionals aiming to validate their skills in implementing and managing DevOps processes on the Google Cloud Platform. To increase your chances of passing, practicing with real exam questions shared by those who have succeeded can be invaluable. In this guide, we’ll provide you with practice test questions and answers offering insights directly from candidates who have already passed the exam.
Exam Details:
-
Exam Name: Professional Cloud DevOps Engineer
-
Length of test: 2 hours (120 minutes)
-
Exam Format: Multiple-choice and multiple-select questions
-
Exam Language: English
-
Number of questions in the actual exam: 50-60 questions
-
Passing Score: 70%
Why Use Professional Cloud DevOps Engineer Practice Test?
-
Real Exam Experience: Our practice tests accurately replicate the format and difficulty of the actual Professional Cloud DevOps Engineer exam, providing you with a realistic preparation experience.
-
Identify Knowledge Gaps: Practicing with these tests helps you identify areas where you need more study, allowing you to focus your efforts effectively.
-
Boost Confidence: Regular practice with exam-like questions builds your confidence and reduces test anxiety.
-
Track Your Progress: Monitor your performance over time to see your improvement and adjust your study plan accordingly.
Key Features of Professional Cloud DevOps Engineer Practice Test:
-
Up-to-Date Content: Our community ensures that the questions are regularly updated to reflect the latest exam objectives and technology trends.
-
Detailed Explanations: Each question comes with detailed explanations, helping you understand the correct answers and learn from any mistakes.
-
Comprehensive Coverage: The practice tests cover all key topics of the Professional Cloud DevOps Engineer exam, including CI/CD pipelines, site reliability engineering, and observability practices.
-
Customizable Practice: Create your own practice sessions based on specific topics or difficulty levels to tailor your study experience to your needs.
Use the member-shared Professional Cloud DevOps Engineer Practice Tests to ensure you're fully prepared for your certification exam. Start practicing today and take a significant step towards achieving your certification goals!
Related questions
You are creating a CI/CD pipeline to perform Terraform deployments of Google Cloud resources Your CI/CD tooling is running in Google Kubernetes Engine (GKE) and uses an ephemeral Pod for each pipeline run You must ensure that the pipelines that run in the Pods have the appropriate Identity and Access Management (1AM) permissions to perform the Terraform deployments You want to follow Google-recommended practices for identity management What should you do?
Choose 2 answers
Explanation:
The best options for ensuring that the pipelines that run in the Pods have the appropriate IAM permissions to perform the Terraform deployments are to create a new Kubernetes service account and assign the service account to the Pods, and to use Workload Identity to authenticate as the Google service account. A Kubernetes service account is an identity that represents an application or a process running in a Pod. A Google service account is an identity that represents a Google Cloud resource or service. Workload Identity is a feature that allows you to bind Kubernetes service accounts to Google service accounts. By using Workload Identity, you can avoid creating and managing JSON service account keys, which are less secure and require more maintenance. You can also assign the appropriate IAM permissions to the Google service account that corresponds to the Kubernetes service account.
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?
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.
You are creating Cloud Logging sinks to export log entries from Cloud Logging to BigQuery for future analysis Your organization has a Google Cloud folder named Dev that contains development projects and a folder named Prod that contains production projects Log entries for development projects must be exported to dev_dataset. and log entries for production projects must be exported to prod_dataset You need to minimize the number of log sinks created and you want to ensure that the log sinks apply to future projects What should you do?
Explanation:
The best option for minimizing the number of log sinks created and ensuring that the log sinks apply to future projects is to create an aggregated log sink in the Dev and Prod folders. An aggregated log sink is a log sink that collects logs from multiple sources, such as projects, folders, or organizations. By creating an aggregated log sink in each folder, you can export log entries for development projects to dev_dataset and log entries for production projects to prod_dataset. You can also use filters to specify which logs you want to export. Additionally, by creating an aggregated log sink at the folder level, you can ensure that the log sink applies to future projects that are created under that folder.
Your team is designing a new application for deployment into Google Kubernetes Engine (GKE). You need to set up monitoring to collect and aggregate various application-level metrics in a centralized location. You want to use Google Cloud Platform services while minimizing the amount of work required to set up monitoring. What should you do?
Explanation:
https://cloud.google.com/kubernetes-engine/docs/concepts/custom-and-external-metrics#custom_metrics
https://github.com/GoogleCloudPlatform/k8s-stackdriver/blob/master/custom-metrics-stackdriver-adapter/README.md
Your application can report a custom metric to Cloud Monitoring. You can configure Kubernetes to respond to these metrics and scale your workload automatically. For example, you can scale your application based on metrics such as queries per second, writes per second, network performance, latency when communicating with a different application, or other metrics that make sense for your workload. https://cloud.google.com/kubernetes-engine/docs/concepts/custom-and-external-metrics
You are managing an application that exposes an HTTP endpoint without using a load balancer. The latency of the HTTP responses is important for the user experience. You want to understand what HTTP latencies all of your users are experiencing. You use Stackdriver Monitoring. What should you do?
Explanation:
https://sre.google/workbook/implementing-slos/
https://cloud.google.com/architecture/adopting-slos/
Latency is commonly measured as a distribution. Given a distribution, you can measure various percentiles. For example, you might measure the number of requests that are slower than the historical 99th percentile.
You are performing a semiannual capacity planning exercise for your flagship service. You expect a service user growth rate of 10% month-over-month over the next six months. Your service is fully containerized and runs on Google Cloud Platform (GCP). using a Google Kubernetes Engine (GKE) Standard regional cluster on three zones with cluster autoscaler enabled. You currently consume about 30% of your total deployed CPU capacity, and you require resilience against the failure of a zone. You want to ensure that your users experience minimal negative impact as a result of this growth or as a result of zone failure, while avoiding unnecessary costs. How should you prepare to handle the predicted growth?
Explanation:
https://cloud.google.com/kubernetes-engine/docs/concepts/horizontalpodautoscaler
The Horizontal Pod Autoscaler changes the shape of your Kubernetes workload by automatically increasing or decreasing the number of Pods in response to the workload's CPU or memory consumption
You are managing an application that runs in Compute Engine The application uses a custom HTTP server to expose an API that is accessed by other applications through an internal TCP/UDP load balancer A firewall rule allows access to the API port from 0.0.0-0/0. You need to configure Cloud Logging to log each IP address that accesses the API by using the fewest number of steps What should you do Bret?
Explanation:
The best option for configuring Cloud Logging to log each IP address that accesses the API by using the fewest number of steps is to enable logging on the firewall rule. A firewall rule is a rule that controls the traffic to and from your Compute Engine instances. You can enable logging on a firewall rule to capture information about the traffic that matches the rule, such as source and destination IP addresses, protocols, ports, and actions. You can use Cloud Logging to view and export the firewall logs to other destinations, such as BigQuery, for further analysis.
Your company runs applications in Google Kubernetes Engine (GKE). Several applications rely on ephemeral volumes. You noticed some applications were unstable due to the DiskPressure node condition on the worker nodes. You need to identify which Pods are causing the issue, but you do not have execute access to workloads and nodes. What should you do?
Unlock Premium Member
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?
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.
You support an application running on App Engine. The application is used globally and accessed from various device types. You want to know the number of connections. You are using Stackdriver Monitoring for App Engine. What metric should you use?
Explanation:
https://cloud.google.com/monitoring/api/metrics_gcp#gcp-appengine
Question