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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 11, 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

Your company’s on-premises Apache Hadoop servers are approaching end-of-life, and IT has decided to migrate the cluster to Google Cloud Dataproc. A like-for-like migration of the cluster would require 50 TB of Google Persistent Disk per node. The CIO is concerned about the cost of using that much block storage. You want to minimize the storage cost of the migration. What should you do?

Options:

A.

Put the data into Google Cloud Storage.

B.

Use preemptible virtual machines (VMs) for the Cloud Dataproc cluster.

C.

Tune the Cloud Dataproc cluster so that there is just enough disk for all data.

D.

Migrate some of the cold data into Google Cloud Storage, and keep only the hot data in Persistent Disk.

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Question 2

MJTelco needs you to create a schema in Google Bigtable that will allow for the historical analysis of the last 2 years of records. Each record that comes in is sent every 15 minutes, and contains a unique identifier of the device and a data record. The most common query is for all the data for a given device for a given day. Which schema should you use?

Options:

A.

Rowkey: date#device_idColumn data: data_point

B.

Rowkey: dateColumn data: device_id, data_point

C.

Rowkey: device_idColumn data: date, data_point

D.

Rowkey: data_pointColumn data: device_id, date

E.

Rowkey: date#data_pointColumn data: device_id

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.