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Google Professional Data Engineer Exam

Last Update Nov 9, 2025
Total Questions : 387 With Comprehensive Analysis

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Last Update Nov 9, 2025
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The Professional-Data-Engineer Google Cloud Certified exam consists of multiple-choice and multiple-select questions. The exam duration is 2 hours, and the passing score is 70%.
The Professional-Data-Engineer Google Cloud Certified exam tests your ability to design, build, and manage data systems on Google Cloud. It covers data processing, machine learning, security, and optimization.
ClapGeek offers a range of study materials, including comprehensive Professional-Data-Engineer exam dumps, practice tests, and expertly crafted study guides. Our Professional-Data-Engineer exam materials cover all topics required for the Google Professional-Data-Engineer certification.
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The Google Professional-Data-Engineer and Cloud-Digital-Leader exams cater to different audiences and skill levels within the Google Cloud ecosystem:

  • Google Professional-Data-Engineer Exam: The Google Professional-Data-Engineer Exam focuses on advanced data engineering tasks, such as designing, building, and maintaining scalable data processing systems and implementing machine learning models on Google Cloud.
  • Google Cloud-Digital-Leader Exam: The Google Cloud-Digital-Leader Exam centers on foundational knowledge of cloud computing and understanding how Google Cloud products and services can be leveraged to achieve business objectives.
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Google Professional Data Engineer Exam Questions and Answers

Questions 1

Flowlogistic wants to use Google BigQuery as their primary analysis system, but they still have Apache Hadoop and Spark workloads that they cannot move to BigQuery. Flowlogistic does not know how to store the data that is common to both workloads. What should they do?

Options:

A.

Store the common data in BigQuery as partitioned tables.

B.

Store the common data in BigQuery and expose authorized views.

C.

Store the common data encoded as Avro in Google Cloud Storage.

D.

Store he common data in the HDFS storage for a Google Cloud Dataproc cluster.

Questions 2

Flowlogistic’s CEO wants to gain rapid insight into their customer base so his sales team can be better informed in the field. This team is not very technical, so they’ve purchased a visualization tool to simplify the creation of BigQuery reports. However, they’ve been overwhelmed by all thedata in the table, and are spending a lot of money on queries trying to find the data they need. You want to solve their problem in the most cost-effective way. What should you do?

Options:

A.

Export the data into a Google Sheet for virtualization.

B.

Create an additional table with only the necessary columns.

C.

Create a view on the table to present to the virtualization tool.

D.

Create identity and access management (IAM) roles on the appropriate columns, so only they appear in a query.

Questions 3

Flowlogistic is rolling out their real-time inventory tracking system. The tracking devices will all send package-tracking messages, which will now go to a single Google Cloud Pub/Sub topic instead of the Apache Kafka cluster. A subscriber application will then process the messages for real-time reporting and store them in Google BigQuery for historical analysis. You want to ensure the package data can be analyzed over time.

Which approach should you take?

Options:

A.

Attach the timestamp on each message in the Cloud Pub/Sub subscriber application as they are received.

B.

Attach the timestamp and Package ID on the outbound message from each publisher device as they are sent to Clod Pub/Sub.

C.

Use the NOW () function in BigQuery to record the event’s time.

D.

Use the automatically generated timestamp from Cloud Pub/Sub to order the data.

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