Google GCP-DE Exam Details & Actual Exam Questions

  • Exam Code/Number: GCP-DE
  • Exam Name/Title: Data Engineer
  • Certification Provider: Google
  • Corresponding Certification: Google Cloud Certified
  • Exam Questions: 77
  • Updated On: Jul,20 2026
  • Certification Level: Professional

Google Data Engineer Exam Questions

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Google GCP-DE Exam Overview:

Certification Vendor:Google Cloud
Exam Name:Google Cloud Professional Data Engineer Certification Exam
Exam Number:PDE
Related Certifications:Google Cloud Associate Cloud Engineer
Google Cloud Professional Machine Learning Engineer
Passing Score:Not publicly disclosed
Real Exam Qty:Approximately 50–60
Exam Duration:120 minutes
Exam Format:Multiple choice, Multiple select, Proctored online or onsite (Kryterion Webassessor)
Available Languages:English, Japanese
Certificate Validity Period:2 years
Exam Price:$200 USD
Recommended Training:Google Cloud Skills Boost - Data Engineering Path
Coursera - Google Cloud Data Engineering Professional Certificate
Exam Registration:Kryterion Webassessor Portal
Google Cloud Certification Registration
Sample Questions:Google GCP-DE Sample Questions
Exam Way:Online proctored or test center (Kryterion Webassessor)
Pre Condition:No mandatory prerequisites, but Associate Cloud Engineer or equivalent experience recommended
Official Syllabus URL:https://cloud.google.com/certification/data-engineer

Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Designing data processing systems20%- Storage and data modeling
  • 1. Data warehouse design using BigQuery
    • 2. Data lake architecture on Google Cloud Storage
      - Data pipeline architecture design
      • 1. Batch vs streaming data processing selection
        • 2. Scalable data ingestion design
          Topic 2: Maintaining and optimizing data and ML solutions20%- Security and governance
          • 1. IAM and access control
            • 2. Data encryption and compliance
              - Machine learning integration
              • 1. BigQuery ML usage
                • 2. Vertex AI integration for pipelines
                  Topic 3: Building and operationalizing data processing systems30%- Data pipeline implementation
                  • 1. Dataproc and Spark-based processing
                    • 2. Dataflow pipeline development
                      - Data ingestion and transformation
                      • 1. Pub/Sub streaming ingestion
                        • 2. ETL/ELT workflows
                          Topic 4: Operationalizing data and ML pipelines30%- Pipeline automation and orchestration
                          • 1. Scheduling and monitoring pipelines
                            • 2. Cloud Composer workflows
                              - Monitoring and troubleshooting
                              • 1. Logging and observability
                                • 2. Performance optimization and debugging


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