Free PDF Quiz 2026 Professional-Cloud-Developer: Trustable Certification Google Certified Professional - Cloud Developer Questions

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Google Professional-Cloud-Developer Exam Syllabus Topics:

Section	Objectives
Integrate applications with Google Cloud services	- Integrate with data and storage services 1. Use messaging and event-driven services 2. Integrate with Google Cloud APIs 3. Implement authentication and authorization
Design scalable, available, and reliable cloud-native applications	- Design highly performant applications and APIs 1. Choose storage options for application data 2. Design resilient and scalable application architectures 3. Design secure applications
Build and test applications	- Manage development environments 1. Implement CI/CD pipelines 2. Build application artifacts 3. Perform application testing
Manage application performance monitoring	- Monitor and troubleshoot applications 1. Analyze metrics and alerts 2. Optimize application performance 3. Configure logging and tracing
Deploy applications	- Deploy applications to Google Cloud 1. Manage application rollout strategies 2. Deploy applications using Cloud Run 3. Deploy applications to Google Kubernetes Engine

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Google Certified Professional - Cloud Developer Sample Questions (Q347-Q352):

NEW QUESTION # 347
You need to migrate a standalone Java application running in an on-premises Linux virtual machine (VM) to Google Cloud in a cost-effective manner. You decide not to take the lift-and-shift approach, and instead you plan to modernize the application by converting it to a container. How should you accomplish this task?

A. Use Migrate for Anthos to migrate the VM to your Google Kubernetes Engine (GKE) cluster as a container.
B. Use Jib to build a Docker image from your source code, and upload it to Artifact Registry. Deploy the application in a GKE cluster, and test the application.
C. Export the VM as a raw disk and import it as an image. Create a Compute Engine instance from the Imported image.
D. Use Migrate for Compute Engine to migrate the VM to a Compute Engine instance, and use Cloud Build to convert it to a container.

Answer: B

Explanation:
Explanation
https://cloud.google.com/blog/products/application-development/introducing-jib-build-java-docker-images-bette

NEW QUESTION # 348
You are a developer working with the CI/CD team to troubleshoot a new feature that your team introduced.
The CI/CD team used HashiCorp Packer to create a new Compute Engine image from your development branch. The image was successfully built, but is not booting up. You need to investigate the issue with the CI/CD team. What should you do?

A. Check Compute Engine OS logs using the serial port, and check the Cloud Logging logs to confirm access to the serial port.
B. Create a new feature branch, and ask the build team to rebuild the image.
C. Install Packer locally, build the Compute Engine image locally, and then run it in your personal Google Cloud project.
D. Shut down the deployed virtual machine, export the disk, and then mount the disk locally to access the boot logs.

Answer: A

Explanation:
https://cloud.google.com/compute/docs/troubleshooting/troubleshooting-using-serial-console

NEW QUESTION # 349
Your team is writing a backend application to implement the business logic for an interactive voice response (IVR) system that will support a payroll application. The IVR system has the following technical characteristics:
* Each customer phone call is associated with a unique IVR session.
* The IVR system creates a separate persistent gRPC connection to the backend for each session.
* If the connection is interrupted, the IVR system establishes a new connection, causing a slight latency for that call.
You need to determine which compute environment should be used to deploy the backend application. Using current call data, you determine that:
* Call duration ranges from 1 to 30 minutes.
* Calls are typically made during business hours.
* There are significant spikes of calls around certain known dates (e.g., pay days), or when large payroll changes occur.
You want to minimize cost, effort, and operational overhead. Where should you deploy the backend application?

A. Google Kubernetes Engine cluster in Standard mode
B. Cloud Functions
C. Compute Engine
D. Cloud Run

Answer: D

Explanation:
This page shows Cloud Run-specific details for developers who want to use gRPC to connect a Cloud Run service with other services, for example, to provide simple, high performance communication between internal microservices. You can use all gRPC types, streaming or unary, with Cloud Run.
Possible use cases include:
Communication between internal microservices.
High loads of data (gRPC uses protocol buffers, which are up to seven times faster than REST calls).
Only a simple service definition is needed, you don't want to write a full client library.
Use streaming gRPCs in your gRPC server to build more responsive applications and APIs.
https://cloud.google.com/run/docs/tutorials/secure-services#:~:text=The%20backend%20service%20is%20priva

NEW QUESTION # 350
You are developing an event-driven application. You have created a topic to receive messages sent to Pub/Sub. You want those messages to be processed in real time. You need the application to be independent from any other system and only incur compute costs when new messages arrive. You want to configure the simplest and most efficient architecture What should you do?

A. Deploy the application on Compute Engine. Use a Pub/Sub push subscription to process new messages in the topic.
B. Deploy your code on Cloud Functions. Use a Pub/Sub trigger to handle new messages in the topic.
C. Deploy your code on Cloud Functions. Use a Pub/Sub trigger to invoke the Cloud Function. Use the Pub/Sub API to create a pull subscription to the Pub/Sub topic and read messages from it.
D. Deploy the application on Google Kubernetes Engine. Use the Pub/Sub API to create a pull subscription to the Pub/Sub topic and read messages from it

Answer: B

Explanation:
https://cloud.google.com/functions/docs/calling/pubsub

NEW QUESTION # 351
You deployed a new application to Google Kubernetes Engine and are experiencing some performance degradation. Your logs are being written to Cloud Logging, and you are using a Prometheus sidecar model for capturing metrics. You need to correlate the metrics and data from the logs to troubleshoot the performance issue and send real-time alerts while minimizing costs.
What should you do?

A. Create custom metrics from the Cloud Logging logs, and use Prometheus to import the results using the Cloud Monitoring REST API.
B. Export the Cloud Logging logs and stream the Prometheus metrics to BigQuery. Run a recurring query to join the results, and send notifications using Cloud Tasks.
C. Export the Prometheus metrics and use Cloud Monitoring to view them as external metrics.Configure Cloud Monitoring to create log-based metrics from the logs, and correlate them with the Prometheus data.
D. Export the Cloud Logging logs and the Prometheus metrics to Cloud Bigtable. Run a query to join the results, and analyze in Google Data Studio.

Answer: C

Explanation:
https://cloud.google.com/stackdriver/docs/solutions/gke/prometheus#viewing_metrics

NEW QUESTION # 352
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