Google Professional-Data-Engineer Practice Exam Dumps 2025

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Free Professional-Data-Engineer Exam Dumps

Google Professional-Data-Engineer Exam Dumps FAQs

Q. # 1: What is the Google Professional-Data-Engineer Exam?

The Google Professional-Data-Engineer certification validates your ability to design, build, operationalize, secure, and monitor data processing systems on Google Cloud.

Q. # 2: Who should take the Google Professional-Data-Engineer Exam?

The Professional-Data-Engineer exam is targeted at data engineers, data analysts, machine learning engineers, and cloud architects who want to demonstrate their expertise in managing data solutions on Google Cloud Platform.

Q. # 3: What topics are covered in the Google Professional-Data-Engineer Exam?

The exam covers:

Designing data processing systems
Building and operationalizing data pipelines
Managing data solutions
Ensuring solution quality
Leveraging machine learning models

Q. # 4: What is the format of the Professional-Data-Engineer Exam?

The Professional-Data-Engineer exam is multiple-choice and multiple-select, delivered online or at a testing center via Kryterion.

Q. # 5: Are there any prerequisites for the Professional-Data-Engineer Exam?

There are no formal prerequisites, but Google recommends 3+ years of industry experience, including 1+ year with Google Cloud.

Q. # 6: What is the difference between Google Professional-Data-Engineer and Associate-Cloud-Engineer Exam?

The Google Professional Data Engineer and Associate Cloud Engineer exams differ mainly in focus, difficulty level, and job roles.

The Associate Cloud Engineer certification is entry-level, designed for professionals who deploy, manage, and maintain applications on Google Cloud Platform (GCP). It validates general cloud operations, setup, and configuration skills.

The Professional Data Engineer, on the other hand, is an advanced-level certification focused on designing, building, and managing data processing systems, data analytics, and machine learning models using GCP services like BigQuery, Dataflow, Dataproc, and Pub/Sub.

Q. # 7: What is the difficulty level of the Professional-Data-Engineer Exam?

The Professional-Data-Engineer exam is considered moderate to advanced, requiring hands-on experience with GCP data services and machine learning workflows.

Q. # 8: Where can I find Google Professional-Data-Engineer exam dumps and practice tests?

Visit CertsTopics for verified Professional-Data-Engineer exam dumps, questions and answers, and practice tests that mirror the real exam and come with a success guarantee.

Q. # 9: Is there a success guarantee with CertsTopics materials?

Yes, CertsTopics provides a success guarantee with regularly updated Professional-Data-Engineer dumps material crafted by certified professionals to help you pass on your first attempt.

Google Professional Data Engineer Exam Questions and Answers

Question 1

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:

Rowkey: date#device_idColumn data: data_point

Rowkey: dateColumn data: device_id, data_point

Rowkey: device_idColumn data: date, data_point

Rowkey: data_pointColumn data: device_id, date

Rowkey: date#data_pointColumn data: device_id

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Question 2

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., ]

Question 3

You need to compose visualizations for operations teams with the following requirements:

Which approach meets the requirements?

Options:

Load the data into Google Sheets, use formulas to calculate a metric, and use filters/sorting to show only suboptimal links in a table.

Load the data into Google BigQuery tables, write Google Apps Script that queries the data, calculates the metric, and shows only suboptimal rows in a table in Google Sheets.

Load the data into Google Cloud Datastore tables, write a Google App Engine Application that queries all rows, applies a function to derive the metric, and then renders results in a table using the Google charts and visualization API.

Load the data into Google BigQuery tables, write a Google Data Studio 360 report that connects to your data, calculates a metric, and then uses a filter expression to show only suboptimal rows in a table.

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Google Professional-Data-Engineer Exam Dumps FAQs

Q. # 1: What is the Google Professional-Data-Engineer Exam?

Q. # 2: Who should take the Google Professional-Data-Engineer Exam?

Q. # 3: What topics are covered in the Google Professional-Data-Engineer Exam?

Q. # 4: What is the format of the Professional-Data-Engineer Exam?

Q. # 5: Are there any prerequisites for the Professional-Data-Engineer Exam?

Q. # 6: What is the difference between Google Professional-Data-Engineer and Associate-Cloud-Engineer Exam?

Q. # 7: What is the difficulty level of the Professional-Data-Engineer Exam?

Q. # 8: Where can I find Google Professional-Data-Engineer exam dumps and practice tests?

Q. # 9: Is there a success guarantee with CertsTopics materials?

Google Professional Data Engineer Exam Questions and Answers

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