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Updated: Aug 20, 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
Related Certifications:Google Cloud Associate Cloud Engineer
Google Cloud Professional Machine Learning Engineer
Exam Price:$200 USD
Exam Duration:120 minutes
Certificate Validity Period:2 years
Available Languages:English, Japanese
Real Exam Qty:Approximately 50–60
Passing Score:Not publicly disclosed
Exam Format:Multiple choice, Multiple select, Proctored online or onsite (Kryterion Webassessor)
Recommended Training:Coursera - Google Cloud Data Engineering Professional Certificate
Google Cloud Skills Boost - Data Engineering Path
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%- Machine learning integration
  • 1. BigQuery ML usage
    • 2. Vertex AI integration for pipelines
      - Security and governance
      • 1. Data encryption and compliance
        • 2. IAM and access control
          Topic 2: Operationalizing data and ML pipelines30%- Monitoring and troubleshooting
          • 1. Performance optimization and debugging
            • 2. Logging and observability
              - Pipeline automation and orchestration
              • 1. Cloud Composer workflows
                • 2. Scheduling and monitoring pipelines
                  Topic 3: Building and operationalizing data processing systems30%- Data ingestion and transformation
                  • 1. Pub/Sub streaming ingestion
                    • 2. ETL/ELT workflows
                      - Data pipeline implementation
                      • 1. Dataproc and Spark-based processing
                        • 2. Dataflow pipeline development
                          Topic 4: 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

                                  Google Data Engineer Sample Questions:

                                  1. When a Cloud Bigtable node fails, is lost.

                                  A) all data
                                  B) the last transaction
                                  C) no data
                                  D) the time dimension


                                  2. If you're running a performance test that depends upon Cloud Bigtable, all the choices except one below are recommended steps. Which is NOT a recommended step to follow?

                                  A) Use at least 300 GB of data.
                                  B) Before you test, run a heavy pre-test for several minutes.
                                  C) Do not use a production instance.
                                  D) Run your test for at least 10 minutes.


                                  3. You need to create a data pipeline that copies time-series transaction data so that it can be queried from within BigQuery by your data science team for analysis. Every hour, thousands of transactions are updated with a new status. The size of the intitial dataset is 1.5 PB, and it will grow by 3 TB per day. The data is heavily structured, and your data science team will build machine learning models based on this dat a. You want to maximize performance and usability for your data science team. Which two strategies should you adopt? Choose 2 answers.

                                  A) Develop a data pipeline where status updates are appended to BigQuery instead of updated.
                                  B) Preserve the structure of the data as much as possible.
                                  C) Denormalize the data as must as possible.
                                  D) Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro fil
                                  E) Use BigQuery'ssupport for external data sources to query.
                                  F) Use BigQuery UPDATE to further reduce the size of the dataset.


                                  4. Your company maintains a hybrid deployment with GCP, where analytics are performed on your anonymized customer dat a. The data are imported to Cloud Storage from your data center through parallel uploads to a data transfer server running on GCP. Management informs you that the daily transfers take too long and have asked you to fix the problem. You want to maximize transfer speeds. Which action should you take?

                                  A) Increase the CPU size on your server.
                                  B) Increase the size of the Google Persistent Disk on your server.
                                  C) Increase your network bandwidth from Compute Engine to Cloud Storage.
                                  D) Increase your network bandwidth from your datacenter to GCP.


                                  5. 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 Local SSD
                                  B) Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage
                                  C) Cloud Load Balancing, Cloud Dataflow, and Cloud Storage
                                  D) Cloud Pub/Sub, Cloud SQL, and Cloud Storage


                                  Solutions:

                                  Question # 1
                                  Answer: C
                                  Question # 2
                                  Answer: C
                                  Question # 3
                                  Answer: A,D
                                  Question # 4
                                  Answer: D
                                  Question # 5
                                  Answer: D

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