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Professional-Cloud-Architect Exam Dumps : Google Certified Professional - Cloud Architect (GCP)

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Google Professional-Cloud-Architect Exam Dumps FAQs

Q. # 1: What is the Google Professional-Cloud-Architect Certification?

The Google Professional-Cloud-Architect certification validates your expertise in designing, developing, and managing secure, scalable, and cost-effective cloud solutions using Google Cloud Platform (GCP).

Q. # 2: Who is the target audience for the Google Professional-Cloud-Architect Exam?

The Google Professional-Cloud-Architect Exam targets GCP professionals, cloud architects, and technical leads with at least three years of industry experience, including one year designing and managing GCP solutions.

Q. # 3: What topics are covered in the Google Professional-Cloud-Architect Exam?

The Google Professional-Cloud-Architect Exam covers six domains: Designing and planning cloud solution architecture Managing and provisioning infrastructure Designing for security and compliance Analyzing and optimizing processes Managing implementation Ensuring solution and operations reliability.

Q. # 4: How many questions are included in the Google Professional-Cloud-Architect Exam?

The Google Professional-Cloud-Architect Exam includes 5060 multiple-choice and multiple-select questions, with 2 case studies that account for 2030% of the total exam content.

Q. # 5: What is the duration of the Google Professional-Cloud-Architect Exam?

The Google Professional-Cloud-Architect Exam lasts 120 minutes, giving candidates two hours to complete all questions and case studies.

Q. # 6: What is the passing criteria for the Google Professional-Cloud-Architect Exam?

Google provides a pass/fail result only, without disclosing an exact score, though it's widely estimated that a score of around 70% is required to pass.

Q. # 7: What is the difference between Google Professional-Cloud-Architect and Cloud-Digital-Leader Exams?

The Difference Between Google Professional-Cloud-Architect and Cloud-Digital-Leader Exams:

  • Google Professional-Cloud-Architect Exam: The Google Professional-Cloud-Architect Exam emphasizes technical expertise in designing, managing, and securing scalable cloud solutions on Google Cloud. It targets professionals who architect enterprise-grade systems and align cloud strategies with business goals.
  • Google Cloud-Digital-Leader Exam: The Google Cloud-Digital-Leader Exam focuses on foundational cloud knowledge, including digital transformation, cloud economics, and Google Cloud services. It’s designed for non-technical stakeholders who need to understand how cloud supports business innovation.

Q. # 8: How can CertsTopics help me prepare for the Google Professional-Cloud-Architect Exam?

CertsTopics offers Professional-Cloud-Architect PDFs and testing engine materials featuring Exam Dumps, Questions and Answers, and Practice Tests with a success guarantee to help you pass confidently.

Q. # 9: Is there a refund policy if I dont pass the Google Professional-Cloud-Architect Exam?

While we offer a success guarantee, please refer to our refund policy page on CertsTopics for full details.

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

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Question 1

For this question refer to the TerramEarth case study

Operational parameters such as oil pressure are adjustable on each of TerramEarth's vehicles to increase their efficiency, depending on their environmental conditions. Your primary goal is to increase the operating efficiency of all 20 million cellular and unconnected vehicles in the field How can you accomplish this goal?

Options:

A.

Have your engineers inspect the data for patterns, and then create an algorithm with rules that make operational adjustments automatically.

B.

Capture all operating data, train machine learning models that identify ideal operations, and run locally to make operational adjustments automatically.

C.

Implement a Google Cloud Dataflow streaming job with a sliding window, and use Google Cloud Messaging (GCM) to make operational adjustments automatically.

D.

Capture all operating data, train machine learning models that identify ideal operations, and host in Google Cloud Machine Learning (ML) Platform to make operational adjustments automatically.

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Question 2

For this question, refer to the Dress4Win case study.

As part of their new application experience, Dress4Wm allows customers to upload images of themselves. The customer has exclusive control over who may view these images. Customers should be able to upload images with minimal latency and also be shown their images quickly on the main application page when they log in. Which configuration should Dress4Win use?

Options:

A.

Store image files in a Google Cloud Storage bucket. Use Google Cloud Datastore to maintain metadata that maps each customer's ID and their image files.

B.

Store image files in a Google Cloud Storage bucket. Add custom metadata to the uploaded images in Cloud Storage that contains the customer's unique ID.

C.

Use a distributed file system to store customers' images. As storage needs increase, add more persistent disks and/or nodes. Assign each customer a unique ID, which sets each file's owner attribute, ensuring privacy of images.

D.

Use a distributed file system to store customers' images. As storage needs increase, add more persistent disks and/or nodes. Use a Google Cloud SQL database to maintain metadata that maps each customer's ID to their image files.

Question 3

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

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