Prepare with Google GCP-DE exam practice material, pass for sure

Updated: Aug 29, 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 Professional Machine Learning Engineer
Google Cloud Associate Cloud Engineer
Exam Duration:120 minutes
Certificate Validity Period:2 years
Available Languages:English, Japanese
Exam Price:$200 USD
Exam Format:Proctored online or onsite (Kryterion Webassessor), Multiple select, Multiple choice
Passing Score:Not publicly disclosed
Real Exam Qty:Approximately 50–60
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: 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 2: Maintaining and optimizing data and ML solutions20%- Machine learning integration
          • 1. Vertex AI integration for pipelines
            • 2. BigQuery ML usage
              - Security and governance
              • 1. Data encryption and compliance
                • 2. IAM and access control
                  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: 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

                                  Google Data Engineer Sample Questions:

                                  Question 1

                                  Which is not a valid reason for poor Cloud Bigtable performance?

                                  A. The workload isn't appropriate for Cloud Bigtable.
                                  B. The Cloud Bigtable cluster has too many nodes.
                                  C. There are issues with the network connection.
                                  D. The table's schema is not designed correctly.


                                  Question 2

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

                                  A. Pig
                                  B. YARN
                                  C. Spark
                                  D. Hive


                                  Question 3

                                  You operate an IoT pipeline built around Apache Kafka that normally receives around 5000 messages per second. You want to use Google Cloud Platform to create an alert as soon as the moving average over 1 hour drops below 4000 messages per second. What should you do?

                                  A. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour
                                  B. If that number falls below 4000, send an alert.
                                  C. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
                                  D. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
                                  E. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour
                                  F. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuer
                                  G. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
                                  H. If that number falls below 4000, send an alert.
                                  I. Set a fixed time window of 1 hour.Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
                                  J. Consume the stream of data in Cloud Dataflow using Kafka I
                                  K. Set a sliding time window of 1 hour every 5 minute
                                  L. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtabl
                                  M. Consume the stream of data in Cloud Dataflow using Kafka I


                                  Question 4

                                  When using Cloud Dataproc clusters, you can access the YARN web interface by configuring a browser to connect through a proxy.

                                  A. SOCKS
                                  B. HTTPS
                                  C. VPN
                                  D. HTTP


                                  Question 5

                                  You are creating a new pipeline in Google Cloud to stream IoT data from Cloud Pub/Sub through Cloud Dataflow to BigQuery. While previewing the data, you notice that roughly 2% of the data appears to be corrupt. You need to modify the Cloud Dataflow pipeline to filter out this corrupt data. What should you do?

                                  A. Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.
                                  B. Add a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.
                                  C. Add a SideInput that returns a Boolean if the element is corrupt.
                                  D. Add a ParDo transform in Cloud Dataflow to discard corrupt elements.


                                  Solutions:

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

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