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Google Professional-Cloud-Architect Exam With Confidence Using Practice Dumps

Exam Code:
Professional-Cloud-Architect
Exam Name:
Google Certified Professional - Cloud Architect (GCP)
Certification:
Vendor:
Questions:
277
Last Updated:
Feb 15, 2026
Exam Status:
Stable
Google Professional-Cloud-Architect

Professional-Cloud-Architect: Google Cloud Certified Exam 2025 Study Guide Pdf and Test Engine

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Google Certified Professional - Cloud Architect (GCP) Questions and Answers

Question 1

For this question, refer to the TerramEarth case study.

The TerramEarth development team wants to create an API to meet the company's business requirements. You want the development team to focus their development effort on business value versus creating a custom framework. Which method should they use?

Options:

A.

Use Google App Engine with Google Cloud Endpoints. Focus on an API for dealers and partners.

B.

Use Google App Engine with a JAX-RS Jersey Java-based framework. Focus on an API for the public.

C.

Use Google App Engine with the Swagger (open API Specification) framework. Focus on an API for the public.

D.

Use Google Container Engine with a Django Python container. Focus on an API for the public.

E.

Use Google Container Engine with a Tomcat container with the Swagger (Open API Specification) framework. Focus on an API for dealers and partners.

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Question 2

For this question, refer to the TerramEarth case study.

TerramEarth's 20 million vehicles are scattered around the world. Based on the vehicle's location its telemetry data is stored in a Google Cloud Storage (GCS) regional bucket (US. Europe, or Asia). The CTO has asked you to run a report on the raw telemetry data to determine why vehicles are breaking down after 100 K miles. You want to run this job on all the data. What is the most cost-effective way to run this job?

Options:

A.

Move all the data into 1 zone, then launch a Cloud Dataproc cluster to run the job.

B.

Move all the data into 1 region, then launch a Google Cloud Dataproc cluster to run the job.

C.

Launch a cluster in each region to preprocess and compress the raw data, then move the data into a multi region bucket and use a Dataproc cluster to finish the job.

D.

Launch a cluster in each region to preprocess and compress the raw data, then move the data into a region bucket and use a Cloud Dataproc cluster to finish the jo

Question 3

For this question, refer to the TerramEarth case study.

TerramEarth has equipped unconnected trucks with servers and sensors to collet telemetry data. Next year they want to use the data to train machine learning models. They want to store this data in the cloud while reducing costs. What should they do?

Options:

A.

Have the vehicle’ computer compress the data in hourly snapshots, and store it in a Google Cloud storage (GCS) Nearline bucket.

B.

Push the telemetry data in Real-time to a streaming dataflow job that compresses the data, and store it in Google BigQuery.

C.

Push the telemetry data in real-time to a streaming dataflow job that compresses the data, and store it in Cloud Bigtable.

D.

Have the vehicle's computer compress the data in hourly snapshots, a Store it in a GCS Coldline bucket.