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:

387

Last Updated:

Nov 26, 2025

Exam Status:

Stable

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

Are you worried about passing the Google Professional-Data-Engineer (Google Professional Data Engineer Exam) exam? Download the most recent Google Professional-Data-Engineer braindumps with answers that are 100% real. After downloading the Google Professional-Data-Engineer exam dumps training , you can receive 99 days of free updates, making this website one of the best options to save additional money. In order to help you prepare for the Google Professional-Data-Engineer exam questions and verified answers by IT certified experts, CertsTopics has put together a complete collection of dumps questions and answers. To help you prepare and pass the Google Professional-Data-Engineer exam on your first attempt, we have compiled actual exam questions and their answers.

Our (Google Professional Data Engineer Exam) Study Materials are designed to meet the needs of thousands of candidates globally. A free sample of the CompTIA Professional-Data-Engineer test is available at CertsTopics. Before purchasing it, you can also see the Google Professional-Data-Engineer practice exam demo.

Get Professional-Data-Engineer Full Access

Related Google Exams

Google Professional Data Engineer Exam Questions and Answers

Get Free Professional-Data-Engineer Questions and Answers

Question 1

After migrating ETL jobs to run on BigQuery, you need to verify that the output of the migrated jobs is the same as the output of the original. You’ve loaded a table containing the output of the original job and want to compare the contents with output from the migrated job to show that they are identical. The tables do not contain a primary key column that would enable you to join them together for comparison.

What should you do?

Options:

Select random samples from the tables using the RAND() function and compare the samples.

Select random samples from the tables using the HASH() function and compare the samples.

Use a Dataproc cluster and the BigQuery Hadoop connector to read the data from each table and calculate a hash from non-timestamp columns of the table after sorting. Compare the hashes of each table.

Create stratified random samples using the OVER() function and compare equivalent samples from each table.

Buy Now

Question 2

You are managing a Cloud Dataproc cluster. You need to make a job run faster while minimizing costs, without losing work in progress on your clusters. What should you do?

Options:

Increase the cluster size with more non-preemptible workers.

Increase the cluster size with preemptible worker nodes, and configure them to forcefully decommission.

Increase the cluster size with preemptible worker nodes, and use Cloud Stackdriver to trigger a script to preserve work.

Increase the cluster size with preemptible worker nodes, and configure them to use graceful decommissioning.

Question 3

You have a data analyst team member who needs to analyze data by using BigQuery. The data analyst wants to create a data pipeline that would load 200 CSV files with an average size of 15MB from a Cloud Storage bucket into BigQuery daily. The data needs to be ingested and transformed before being accessed in BigQuery for analysis. You need to recommend a fully managed, no-code solution for the data analyst. What should you do?

Options:

Create a Cloud Run function and schedule it to run daily using Cloud Scheduler to load the data into BigQuery.

Use the BigQuery Data Transfer Service to load files from Cloud Storage to BigQuery, create a BigQuery job which transforms the data using BigQuery SQL and schedule it to run daily.

Build a custom Apache Beam pipeline and run it on Dataflow to load the file from Cloud Storage to BigQuery and schedule it to run daily using Cloud Composer.

Create a pipeline by using BigQuery pipelines and schedule it to load the data into BigQuery daily.

Answer:

Explanation:

The requirements are for a daily scheduled load, ingest, and transformation, and specifically a fully managed, no-code solution.

Ingest (Load): The BigQuery Data Transfer Service (DTS) is the fully managed, serverless, and no-code solution for batch loading files (including CSV from Cloud Storage) into BigQuery on a schedule. This is the "ingest" part.

Transform: After loading the raw data into a staging table using DTS, the transformation can be done using BigQuery SQL. This transformation query can then be automated using a Scheduled Query in BigQuery, which is also a fully managed and no-code feature that runs on a schedule.

Fully Managed & No-Code: Both DTS for Cloud Storage and Scheduled Queries are native BigQuery features that are fully managed and configured through the console without requiring code, directly meeting the constraints.

Correcting other options:

A (Cloud Run + Script): Cloud Run requires writing a custom Python script, which violates the no-code requirement.

C (Dataflow + Apache Beam + Cloud Composer): This is a powerful, highly scalable ETL solution, but it requires writing custom code (Apache Beam) and requires setting up and managing a workflow orchestrator (Cloud Composer/Airflow), which violates both the fully managed (Dataflow is serverless, but the code/pipeline itself is custom and needs maintenance) and no-code requirements.

D (BigQuery pipelines): "BigQuery pipelines" is not a distinct, official product name in the Google Cloud documentation that fulfills a no-code scheduled ETL. The closest product is the combination of DTS and Scheduled Queries, as described in option B.

[Reference: Google Cloud Documentation on BigQuery Data Transfer Service and Scheduled Queries:, "The BigQuery Data Transfer Service automates data movement into BigQuery on a scheduled, managed basis... The BigQuery Data Transfer Service supports loading data from Cloud Storage in one of the following formats: Comma-separated values (CSV)..." (Source: What is BigQuery Data Transfer Service? and Introduction to Cloud Storage transfers), "A scheduled query is a query that BigQuery automatically runs at regular intervals. When you configure a scheduled query, you specify the GoogleSQL SELECT statement to run, the destination table for the query results, and the frequency of the query." (Source: Scheduling queries), This combination delivers a fully managed, no-code ELT (Extract-Load-Transform) pipeline., ]

Get Free Professional-Data-Engineer Questions and Answers

Big Black Friday Sale 70% Discount Offer - Ends in 0d 00h 00m 00s - Coupon code: save70

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

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

Related Google Exams

Google Professional Data Engineer Exam Questions and Answers

Options:

Answer:

Options:

Answer:

Explanation:

Options:

Answer:

Explanation:

CompTIA

Fortinet

Microsoft

Salesforce