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 Professional-Cloud-Architect Dumps with Practice Exam Questions Answers

Questions: 275 questions With Step-by-Step Explanation

Last Update: Dec 20, 2024

Professional-Cloud-Architect Question Includes: Single Choice Questions: 255, Multiple Choice Questions: 20,

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Google Professional-Cloud-Architect Practice Exam FAQs

1. What is the Google Professional-Cloud-Architect Exam?


The Google Professional-Cloud-Architect Exam validates your ability to design, develop, and manage robust, secure, scalable, highly available, and cost-effective solutions on Google Cloud Platform (GCP).

2. Who should take the Google Professional-Cloud-Architect Exam?


The Google Professional-Cloud-Architect exam is ideal for IT professionals, cloud architects, and system administrators who are involved in designing and managing Google Cloud Platform solutions. It is also suitable for those looking to validate their expertise in Google Cloud technologies and advance their careers.

3. What topics does the Google Professional-Cloud-Architect Exam cover?


The Google Professional-Cloud-Architect exam covers a wide range of GCP concepts, including:

  • Designing and planning cloud solutions
  • Managing and provisioning cloud infrastructure
  • Designing for security and compliance
  • Analyzing and optimizing technical and business processes
  • Managing implementation
  • Ensuring solution and operations reliability

4. How many questions are there in the Google Professional-Cloud-Architect Exam?


The Google Professional-Cloud-Architect exam typically consists of 50 multiple-choice questions.

5. What is the passing score for the Google Professional-Cloud-Architect Exam?


The passing score for the Google Professional-Cloud-Architect exam is 70%.

6. What is the difference between Google Professional-Cloud-Architect and Professional-Data-Engineer Exams?


Here's a breakdown of the key differences between the Google Professional-Cloud-Architect and Professional-Data-Engineer exams:

  • Professional-Cloud-Architect Exam: The Google Professional-Cloud-Architect Exam focuses on designing, developing, and managing cloud solutions using Google Cloud technologies. It emphasizes architecture, security, and scalability.
  • Professional-Data-Engineer Exam: The Google Professional-Data-Engineer Exam is centered around data engineering, including designing, building, and maintaining data processing systems. It covers topics like data pipelines, storage, and analytics.

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Professional-Cloud-Architect Questions and Answers

Question # 1

For this question, refer to the TerramEarth case study.

To speed up data retrieval, more vehicles will be upgraded to cellular connections and be able to transmit data to the ETL process. The current FTP process is error-prone and restarts the data transfer from the start of the file when connections fail, which happens often. You want to improve the reliability of the solution and minimize data transfer time on the cellular connections. What should you do?

A.

Use one Google Container Engine cluster of FTP servers. Save the data to a Multi-Regional bucket. Run the ETL process using data in the bucket.

B.

Use multiple Google Container Engine clusters running FTP servers located in different regions. Save the data to Multi-Regional buckets in us, eu, and asia. Run the ETL process using the data in the bucket.

C.

Directly transfer the files to different Google Cloud Multi-Regional Storage bucket locations in us, eu, and asia using Google APIs over HTTP(S). Run the ETL process using the data in the bucket.

D.

Directly transfer the files to a different Google Cloud Regional Storage bucket location in us, eu, and asia using Google APIs over HTTP(S). Run the ETL process to retrieve the data from each Regional bucket.

Question # 2

For this question refer to the TerramEarth case study.

Which of TerramEarth's legacy enterprise processes will experience significant change as a result of increased Google Cloud Platform adoption.

A.

Opex/capex allocation, LAN changes, capacity planning

B.

Capacity planning, TCO calculations, opex/capex allocation

C.

Capacity planning, utilization measurement, data center expansion

D.

Data Center expansion, TCO calculations, utilization measurement

Question # 3

For this question, refer to the TerramEarth case study

Your development team has created a structured API to retrieve vehicle data. They want to allow third parties to develop tools for dealerships that use this vehicle event data. You want to support delegated authorization against this data. What should you do?

A.

Build or leverage an OAuth-compatible access control system.

B.

Build SAML 2.0 SSO compatibility into your authentication system.

C.

Restrict data access based on the source IP address of the partner systems.

D.

Create secondary credentials for each dealer that can be given to the trusted third party.

Question # 4

Your agricultural division is experimenting with fully autonomous vehicles.

You want your architecture to promote strong security during vehicle operation.

Which two architecture should you consider?

Choose 2 answers:

A.

Treat every micro service call between modules on the vehicle as untrusted.

B.

Require IPv6 for connectivity to ensure a secure address space.

C.

Use a trusted platform module (TPM) and verify firmware and binaries on boot.

D.

Use a functional programming language to isolate code execution cycles.

E.

Use multiple connectivity subsystems for redundancy.

F.

Enclose the vehicle's drive electronics in a Faraday cage to isolate chips.

Question # 5

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?

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.