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Google Professional-Cloud-Developer (Google Certified Professional - Cloud Developer) Exam is a certification exam that is designed for individuals who have experience in developing and deploying applications on the Google Cloud Platform. Professional-Cloud-Developer exam is intended to test the candidate's proficiency in designing, building, and deploying scalable and highly available applications using Google Cloud technologies. Google Certified Professional - Cloud Developer certification is recognized globally and is an excellent way to demonstrate your expertise in cloud development.

To prepare for the Professional-Cloud-Developer Exam, candidates should have a solid understanding of cloud computing concepts and be familiar with the Google Cloud platform. They should also have experience in one or more programming languages, such as Java, Python, or JavaScript, as well as experience in developing and deploying applications on Google Cloud.

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Target Audience and Prerequisites

The Google Professional Cloud Developer certification exam is intended for those App Developers who are involved in designing and building applications to run on Google Cloud Platform. These specialists should be familiar with the Google Cloud Platform products, such as Cloud Storage, Compute Engine, Security Key Enforcement, as well as BigQuery.

There are no obligatory requirements that the candidates need to meet to become eligible for the qualifying test. Nevertheless, the target individuals are recommended to have three or more years of industry experience, including one or more years of experience in designing and managing solutions using Google Cloud. Besides that, the students need to develop a comprehensive understanding of the exam topics.

Google Certified Professional - Cloud Developer Sample Questions (Q377-Q382):

NEW QUESTION # 377
You are a developer at a large organization. You are deploying a web application to Google Kubernetes Engine (GKE). The DevOps team has built a CI/CD pipeline that uses Cloud Deploy to deploy the application to Dev Test, and Prod clusters in GKE. After Cloud Deploy successfully deploys the application to the Dev cluster you want to automatically promote it to the Test Cluster. How should you configure this process following Google-recommended best practices?

Answer: C

Explanation:
3 Configure this pipeline to run a deployment step to the Test cluster


NEW QUESTION # 378
Your development team is using Cloud Build to promote a Node.js application built on App Engine from your staging environment to production. The application relies on several directories of photos stored in a Cloud Storage bucket named webphotos-staging in the staging environment. After the promotion, these photos must be available in a Cloud Storage bucket named webphotos-prod in the production environment. You want to automate the process where possible. What should you do?

Answer: A

Explanation:
You should add a build step in the cloudbuild.yaml file before the promotion step with the arguments shown above. This build step will use the gsutil tool to copy the photos from the webphotos-staging bucket to the webphotos-prod bucket. The -r flag tells gsutil to copy all files in the bucket recursively, and the waitFor parameter tells Cloud Build to wait for this step to complete before continuing with the promotion step.


NEW QUESTION # 379
You plan to deploy a new application revision with a Deployment resource to Google Kubernetes Engine (GKE) in production. The container might not work correctly. You want to minimize risk in case there are issues after deploying the revision. You want to follow Google-recommended best practices. What should you do?

Answer: B

Explanation:
https://cloud.google.com/blog/products/containers-kubernetes/ensuring-reliability-and-uptime-for-your-gke-clust Setting PodDisruptionBudget ensures that your workloads have a sufficient number of replicas, even during maintenance. Using the PDB, you can define a number (or percentage) of pods that can be terminated, even if terminating them brings the current replica count below the desired value. With PDB configured, Kubernetes will drain a node following the configured disruption schedule. New pods will be deployed on other available nodes. This approach ensures Kubernetes schedules workloads in an optimal way while controlling the disruption based on the PDB configuration.
https://blog.knoldus.com/how-to-avoid-outages-in-your-kubernetes-cluster-using-pdb/


NEW QUESTION # 380
You have recently instrumented a new application with OpenTelemetry, and you want to check the latency of your application requests in Trace. You want to ensure that a specific request is always traced. What should you do?

Answer: C

Explanation:
Explanation
https://cloud.google.com/trace/docs/setup#force-trace
Cloud Trace doesn't sample every request. To force a specific request to be traced, add an X-Cloud-Trace-Context header to the request.


NEW QUESTION # 381
You recently developed an application that will be hosted on Cloud Run. You need to conduct a load test. You want to analyze the load test logs second by second to understand your Cloud Run service's response to rapid traffic spikes. You want to minimize effort. How should you analyze the logs?

Answer: C

Explanation:
Configuring a log sink to export your Cloud Run logs to BigQuery allows you to perform detailed, second-by-second analysis of the load test logs. BigQuery is well-suited for analyzing large volumes of log data with flexible querying capabilities, which makes it easy to filter, aggregate, and analyze logs to understand your service's response to traffic spikes.
Options like using Cloud Monitoring's log console or estimation from summary charts may provide some insights, but they lack the granularity and advanced querying features needed for precise second-by-second log analysis. Using Cloud SQL with Pub/Sub and Dataflow would require more setup and complexity than simply exporting logs to BigQuery.


NEW QUESTION # 382
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