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Google Professional-Data-Engineer Practice Test Questions Answers

Exam Code: Professional-Data-Engineer (Updated 376 Q&As with Explanation)
Exam Name: Google Professional Data Engineer Exam
Last Update: 13-Jul-2025
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Questions Include:

  • Single Choice: 345 Q&A's
  • Multiple Choice: 31 Q&A's

  • Professional-Data-Engineer Overview

    Google Professional-Data-Engineer Exam Overview

    Category Details
    Certification Name Google Professional Data Engineer – Google Cloud Certified
    Target Audience Data engineers responsible for designing, building, maintaining, and optimizing data architectures and machine learning models on Google Cloud.
    Purpose Validates the skills required to design and manage data processing systems, implement machine learning models, and ensure data security and compliance using Google Cloud tools and services.
    Exam Duration 2 hours
    Number of Questions 50 multiple-choice and multiple-select questions
    Passing Score 70% (35 correct answers out of 50)
    Exam Format Multiple-choice and multiple-select questions
    Exam Delivery Online (via Google Cloud’s exam portal or Pearson VUE)
    Exam Cost $200 (USD)
    Languages Available English
    Prerequisites Recommended: Experience in designing and managing data processing systems, familiarity with Google Cloud Platform services such as BigQuery, Cloud Dataflow, and Cloud ML Engine.
    Key Topics Covered 1. Designing Data Processing Systems
    2. Building and Operationalizing Data Pipelines
    3. Analyzing and Visualizing Data
    4. Machine Learning
    5. Data Security and Compliance
    6. Managing Data Architecture
    Preparation Resources 1. Google Cloud Professional Data Engineer Learning Path
    2. Google Cloud Documentation
    3. Practice exams
    4. Hands-on experience with Google Cloud tools
    Validity Period 2 years (requires recertification after expiration)
    Retake Policy Candidates can retake the exam after 14 days if they do not pass.

     

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    Google Professional-Data-Engineer Exam Topics Breakdown

    Domain Weight (%) Topics Covered
    1. Designing Data Processing Systems 25% - Designing data processing systems using Google Cloud
    - Choosing appropriate tools for data storage and processing
    - Designing scalable and efficient systems
    2. Building and Operationalizing Data Pipelines 25% - Creating and managing data pipelines with tools like Dataflow and Cloud Composer
    - Automating data pipeline operations
    - Integrating data sources and destinations
    3. Analyzing and Visualizing Data 20% - Using BigQuery for data analysis
    - Creating and sharing data visualizations
    - Implementing reporting and dashboards with Looker and other tools
    4. Machine Learning 20% - Applying machine learning models using Google Cloud ML Engine
    - Designing machine learning workflows
    - Using AutoML and TensorFlow for model training
    5. Data Security and Compliance 5% - Implementing data security best practices
    - Managing access control, encryption, and compliance requirements
    6. Managing Data Architecture 5% - Designing and maintaining data architecture for scalability and reliability
    - Ensuring high availability and disaster recovery in data environments

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    Google Professional-Data-Engineer Exam Dumps FAQs

    The Google Professional Data Engineer Exam is a certification exam offered by Google Cloud that validates an individual’s skills and expertise in designing, building, operationalizing, and managing data processing systems and machine learning models on Google Cloud Platform (GCP). This exam is aimed at professionals who have experience working with data systems, and who want to demonstrate their ability to design and implement data solutions using GCP technologies.

    This Google Professional-Data-Engineer exam is intended for professionals who:

    • Have experience working with data engineering and cloud technologies.
    • Are responsible for designing and building data pipelines, managing data infrastructure, and implementing machine learning models in GCP.
    • Want to validate their skills in managing and processing large-scale data systems.
    • Are working in roles such as Data Engineer, Machine Learning Engineer, Data Analyst, or Cloud Architect.

    The Google Professional-Data-Engineer exam tests a wide range of topics related to data engineering and the use of GCP tools. Key topics include:

    • Designing Data Processing Systems: Creating reliable, scalable, and cost-efficient data pipelines and systems using tools like Dataflow, Dataproc, BigQuery, and Cloud Composer.
    • Building and Operationalizing Data Pipelines: Implementing batch and stream processing systems, automating data pipelines, and ensuring that they are robust and reliable.
    • Analyzing and Visualizing Data: Using BigQuery, Cloud Dataprep, and other tools to perform advanced data analysis, query data efficiently, and visualize results.
    • Machine Learning: Leveraging AI Platform and other tools to design, train, and deploy machine learning models.
    • Data Governance and Security: Implementing security measures, managing data access, and ensuring compliance with industry regulations using tools like Cloud Identity & Access Management (IAM), Cloud Key Management, and Cloud Data Loss Prevention.
    • Monitoring and Optimization: Ensuring high performance of data processing systems, optimizing queries, monitoring and troubleshooting systems, and managing costs effectively.
    • Infrastructure Management: Managing resources and services like Google Kubernetes Engine (GKE) and Cloud Functions in the context of data processing.

    The Google Professional Data Engineer Exam is a multiple-choice exam that includes a combination of multiple-choice and multiple-select questions. The exam is designed to test both theoretical knowledge and practical, scenario-based understanding of data engineering on GCP.

    While there are no strict prerequisites, it is highly recommended that candidates have:

    • A solid understanding of cloud computing concepts and data engineering.
    • Practical experience with Google Cloud Platform (GCP) services like BigQuery, Dataflow, Cloud Storage, Cloud Pub/Sub, and AI Platform.
    • Experience with data processing, machine learning, and working with large-scale data systems.
    • Ideally, candidates should have at least 3+ years of industry experience with data engineering and working on GCP.

    The Google Professional Data Engineer Exam is a multiple-choice exam that includes a combination of multiple-choice and multiple-select questions. The exam is designed to test both theoretical knowledge and practical, scenario-based understanding of data engineering on GCP.

    The Google Professional-Data-Engineer exam lasts for 2 hours. You will need to complete all questions within this time frame.

    The passing score for the exam is 70%. You must answer at least 70% of the questions correctly to pass the exam and earn the certification.

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    Professional-Data-Engineer Questions and Answers

    Question # 1

    Flowlogistic’s management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?

    A.

    Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage

    B.

    Cloud Pub/Sub, Cloud Dataflow, and Local SSD

    C.

    Cloud Pub/Sub, Cloud SQL, and Cloud Storage

    D.

    Cloud Load Balancing, Cloud Dataflow, and Cloud Storage

    Question # 2

    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?

    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.

    Question # 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?

    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.

    Question # 4

    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?

    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.

    Question # 5

    Which of the following is NOT true about Dataflow pipelines?

    A.

    Dataflow pipelines are tied to Dataflow, and cannot be run on any other runner

    B.

    Dataflow pipelines can consume data from other Google Cloud services

    C.

    Dataflow pipelines can be programmed in Java

    D.

    Dataflow pipelines use a unified programming model, so can work both with streaming and batch data sources

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