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Google Professional-Data-Engineer Exam With Confidence Using Practice Dumps

Exam Code:
Professional-Data-Engineer
Exam Name:
Google Professional Data Engineer Exam
Certification:
Google Cloud Certified
Vendor:
Google
Questions:
400
Last Updated:
Apr 27, 2026
Exam Status:
Stable
Google Professional-Data-Engineer

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

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Google Professional Data Engineer Exam Questions and Answers

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Question 1

If you're running a performance test that depends upon Cloud Bigtable, all the choices except one below are recommended steps. Which is NOT a recommended step to follow?

Options:

A.

Do not use a production instance.

B.

Run your test for at least 10 minutes.

C.

Before you test, run a heavy pre-test for several minutes.

D.

Use at least 300 GB of data.

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Question 2

Your company has recently grown rapidly and now ingesting data at a significantly higher rate than it was previously. You manage the daily batch MapReduce analytics jobs in Apache Hadoop. However, the recent increase in data has meant the batch jobs are falling behind. You were asked to recommend ways the development team could increase the responsiveness of the analytics without increasing costs. What should you recommend they do?

Options:

A.

Rewrite the job in Pig.

B.

Rewrite the job in Apache Spark.

C.

Increase the size of the Hadoop cluster.

D.

Decrease the size of the Hadoop cluster but also rewrite the job in Hive.

Question 3

You store historic data in Cloud Storage. You need to perform analytics on the historic data. You want to use a solution to detect invalid data entries and perform data transformations that will not require programming or knowledge of SQL.

What should you do?

Options:

A.

Use Cloud Dataflow with Beam to detect errors and perform transformations.

B.

Use Cloud Dataprep with recipes to detect errors and perform transformations.

C.

Use Cloud Dataproc with a Hadoop job to detect errors and perform transformations.

D.

Use federated tables in BigQuery with queries to detect errors and perform transformations.

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