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Google Professional Data Engineer Exam

Last Update Sep 17, 2025
Total Questions : 383 With Comprehensive Analysis

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Last Update Sep 17, 2025
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Google Professional-Data-Engineer Exam Dumps FAQs

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.
Yes, ClapGeek provides Professional-Data-Engineer PDF questions for the Google Professional-Data-Engineer exam. These are accessible after purchase and can be used offline for convenient studying.

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.
Yes, ClapGeek offers a guarantee that you will pass the Google Professional-Data-Engineer exam using our Professional-Data-Engineer exam questions answers and study materials. In case of failure, specific refund policies may apply.
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Google Professional Data Engineer Exam Questions and Answers

Questions 1

You are building a model to predict whether or not it will rain on a given day. You have thousands of input features and want to see if you can improve training speed by removing some features while having a minimum effect on model accuracy. What can you do?

Options:

A.

Eliminate features that are highly correlated to the output labels.

B.

Combine highly co-dependent features into one representative feature.

C.

Instead of feeding in each feature individually, average their values in batches of 3.

D.

Remove the features that have null values for more than 50% of the training records.

Questions 2

Your company is performing data preprocessing for a learning algorithm in Google Cloud Dataflow. Numerous data logs are being are being generated during this step, and the team wants to analyze them. Due to the dynamic nature of the campaign, the data is growing exponentially every hour.

The data scientists have written the following code to read the data for a new key features in the logs.

BigQueryIO.Read

.named(“ReadLogData”)

.from(“clouddataflow-readonly:samples.log_data”)

You want to improve the performance of this data read. What should you do?

Options:

A.

Specify the TableReference object in the code.

B.

Use .fromQuery operation to read specific fields from the table.

C.

Use of both the Google BigQuery TableSchema and TableFieldSchema classes.

D.

Call a transform that returns TableRow objects, where each element in the PCollexction represents a single row in the table.

Questions 3

Your software uses a simple JSON format for all messages. These messages are published to Google Cloud Pub/Sub, then processed with Google Cloud Dataflow to create a real-time dashboard for the CFO. During testing, you notice that some messages are missing in thedashboard. You check the logs, and all messages are being published to Cloud Pub/Sub successfully. What should you do next?

Options:

A.

Check the dashboard application to see if it is not displaying correctly.

B.

Run a fixed dataset through the Cloud Dataflow pipeline and analyze the output.

C.

Use Google Stackdriver Monitoring on Cloud Pub/Sub to find the missing messages.

D.

Switch Cloud Dataflow to pull messages from Cloud Pub/Sub instead of Cloud Pub/Sub pushing messages to Cloud Dataflow.

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