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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:
Vendor:
Questions:
387
Last Updated:
Jan 14, 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

Question 1

You are building a model to make clothing recommendations. You know a user’s fashion preference is likely to change over time, so you build a data pipeline to stream new data back to the model as it becomes available. How should you use this data to train the model?

Options:

A.

Continuously retrain the model on just the new data.

B.

Continuously retrain the model on a combination of existing data and the new data.

C.

Train on the existing data while using the new data as your test set.

D.

Train on the new data while using the existing data as your test set.

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Question 2

You create a new report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. It is company policy to ensure employees can view only the data associated with their region, so you create and populate a table for each region. You need to enforce the regional access policy to the data.

Which two actions should you take? (Choose two.)

Options:

A.

Ensure all the tables are included in global dataset.

B.

Ensure each table is included in a dataset for a region.

C.

Adjust the settings for each table to allow a related region-based security group view access.

D.

Adjust the settings for each view to allow a related region-based security group view access.

E.

Adjust the settings for each dataset to allow a related region-based security group view access.

Question 3

You need to store and analyze social media postings in Google BigQuery at a rate of 10,000 messages per minute in near real-time. Initially, design the application to use streaming inserts for individual postings. Your application also performs data aggregations right after the streaming inserts. You discover that the queries after streaming inserts do not exhibit strong consistency, and reports from the queries might miss in-flight data. How can you adjust your application design?

Options:

A.

Re-write the application to load accumulated data every 2 minutes.

B.

Convert the streaming insert code to batch load for individual messages.

C.

Load the original message to Google Cloud SQL, and export the table every hour to BigQuery via streaming inserts.

D.

Estimate the average latency for data availability after streaming inserts, and always run queries after waiting twice as long.