Google Professional-Data-Engineer Exam Dumps

Get All Google Cloud Certified Professional Data Engineer Exam Questions with Validated Answers

Professional-Data-Engineer Pack
Vendor: Google
Exam Code: Professional-Data-Engineer
Exam Name: Google Cloud Certified Professional Data Engineer
Exam Questions: 401
Last Updated: October 4, 2026
Related Certifications: Google Cloud Certified
Exam Tags: Professional Cloud Administrator
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Free Google Professional-Data-Engineer Exam Actual Questions

Question No. 1

You are planning to migrate your current on-premises Apache Hadoop deployment to the cloud. You need to ensure that the deployment is as fault-tolerant and cost-effective as possible for long-running batch jobs. You want to use a managed service. What should you do?

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Correct Answer: A

Question No. 2

You need to create a new transaction table in Cloud Spanner that stores product sales data. You are deciding what to use as a primary key. From a performance perspective, which strategy should you choose?

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Correct Answer: C

Question No. 3

You have a requirement to insert minute-resolution data from 50,000 sensors into a BigQuery table. You expect significant growth in data volume and need the data to be available within 1 minute of ingestion for real-time analysis of aggregated trends. What should you do?

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Correct Answer: B

Question No. 4

Each analytics team in your organization is running BigQuery jobs in their own projects. You want to enable each team to monitor slot usage within their projects. What should you do?

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Correct Answer: D

Question No. 5

What are two methods that can be used to denormalize tables in BigQuery?

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Correct Answer: B

The conventional method of denormalizing data involves simply writing a fact, along with all its dimensions, into a flat table structure. For example, if you are dealing with sales transactions, you would write each individual fact to a record, along with the accompanying dimensions such as order and customer information.

The other method for denormalizing data takes advantage of BigQuery's native support for nested and repeated structures in JSON or Avro input data. Expressing records using nested and repeated structures can provide a more natural representation of the underlying data. In the case of the sales order, the outer part of a JSON structure would contain the order and customer information, and the inner part of the structure would contain the individual line items of the order, which would be represented as nested, repeated elements.


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