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: 28-Jun-2025
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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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