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Real Google GCP-DE practice exam questions for easy pass!

Updated: Sep 14, 2026

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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
Certificate Validity Period:2 years
Exam Duration:120 minutes
Real Exam Qty:Approximately 50–60
Related Certifications:Google Cloud Associate Cloud Engineer
Google Cloud Professional Machine Learning Engineer
Exam Format:Multiple select, Proctored online or onsite (Kryterion Webassessor), Multiple choice
Available Languages:English, Japanese
Exam Price:$200 USD
Passing Score:Not publicly disclosed
Recommended Training:Google Cloud Skills Boost - Data Engineering Path
Coursera - Google Cloud Data Engineering Professional Certificate
Exam Registration:Google Cloud Certification Registration
Kryterion Webassessor Portal
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: 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 2: Operationalizing data and ML pipelines30%- Monitoring and troubleshooting
          • 1. Logging and observability
            • 2. Performance optimization and debugging
              - Pipeline automation and orchestration
              • 1. Scheduling and monitoring pipelines
                • 2. Cloud Composer workflows
                  Topic 3: 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. Scalable data ingestion design
                        • 2. Batch vs streaming data processing selection
                          Topic 4: 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

                                  Google Data Engineer Sample Questions:

                                  Question #1

                                  Which Cloud Dataflow / Beam feature should you use to aggregate data in an unbounded data source every hour based on the time when the data entered the pipeline?

                                  • A. An hourly watermark
                                  • B. The with Allowed Lateness method
                                  • C. A processing time trigger
                                  • D. An event time trigger
                                  Reveal Solution  Discussion  0

                                  Correct Answer: C  🗳️

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                                  Question #2

                                  Which of the following job types are supported by Cloud Dataproc (select 3 answers)?

                                  • A. Hive
                                  • B. Spark
                                  • C. Pig
                                  • D. YARN
                                  Reveal Solution  Discussion  0

                                  Correct Answer: A,B,C  🗳️

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                                  Question #3

                                  You have historical data covering the last three years in BigQuery and a data pipeline that delivers new data to BigQuery daily. You have noticed that when the Data Science team runs a query filtered on a date column and limited to 30-90 days of data, the query scans the entire table. You also noticed that your bill is increasing more quickly than you expected. You want to resolve the issue as cost-effectively as possible while maintaining the ability to conduct SQL queries. What should you do?

                                  • A. Recommend that the Data Science team use wildcards on the table name suffixes to select the data they need.
                                  • B. Partition the tables by a column containing a TIMESTAMP or DATE Type.
                                  • C. Write an Apache Beam pipeline that creates a BigQuery table per data
                                  • D. Recommend that the Data Science team export the table to a CSV file on Cloud Storage and use Cloud Datalab to explore the data by reading the files directly.
                                  • E. Modify your pipeline to maintain the last 30-90 days of data in one table and the longer history in a different table to minimize full table scans over the entire history.
                                  • F. Re-create the tables using DD
                                  Reveal Solution  Discussion  0

                                  Correct Answer: D  🗳️

                                  Question #4

                                  You are building a model to predict whether or not it will rain on a given day. You have thousands of input features and want to see if you can improve training speed by removing some features while having a minimum effect on model accuracy. What can you do?

                                  • A. Combine highly co-dependent features into one representative feature.
                                  • B. Eliminate features that are highly correlated to the output labels.
                                  • C. Instead of feeding in each feature individually, average their values in batches of 3.
                                  • D. Remove the features that have null values for more than 50% of the training records.
                                  Reveal Solution  Discussion  0

                                  Correct Answer: A  🗳️

                                  Question #5

                                  By default, which of the following windowing behavior does Dataflow apply to unbounded data sets?

                                  • A. Windows at every 100 MB of data
                                  • B. Single, Global Window
                                  • C. Windows at every 1 minute
                                  • D. Windows at every 10 minutes
                                  Reveal Solution  Discussion  0

                                  Correct Answer: B  🗳️

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