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Last Updated: Aug 01, 2026

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Google GCP-DE Exam Syllabus Topics:

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

                                  Google Data Engineer Sample Questions:

                                  1. You are running a pipeline in Cloud Dataflow that receives messages from a Cloud Pub/Sub topic and writes the results to a BigQuery dataset in the EU. Currently, your pipeline is located in europe-west4 and has a maximum of 3 workers, instance type n1-standard-1. You notice that during peak periods, your pipeline is struggling to process records in a timely fashion, when all 3 workers are at maximum CPU utilization. Which two actions can you take to increase performance of your pipeline? (Choose two.)

                                  A) Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Bigtable to BigQuery
                                  B) Create a temporary table in Cloud Spanner that will act as a buffer for new data
                                  C) Increase the number of max workers
                                  D) Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Spanner to BigQuery
                                  E) Create a temporary table in Cloud Bigtable that will act as a buffer for new data
                                  F) Use a larger instance type for your Cloud Dataflow workers
                                  G) Change the zone of your Cloud Dataflow pipeline to run in us-central1


                                  2. You have enabled the free integration between Firebase Analytics and Google BigQuery. Firebase now automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD. You want to query all of the tables for the past 30 days in legacy SQL. What should you do?

                                  A) Use the WHERE_PARTITIONTIME pseudo column
                                  B) Use the TABLE_DATE_RANGE function
                                  C) Use WHERE date BETWEEN YYYY-MM-DD AND YYYY-MM-DD
                                  D) Use SELECT IF.(date >= YYYY-MM-DD AND date <= YYYY-MM-DD


                                  3. You are integrating one of your internal IT applications and Google BigQuery, so users can query BigQuery from the application's interface. You do not want individual users to authenticate to BigQuery and you do not want to give them access to the dataset. You need to securely access BigQuery from your IT application.
                                  What should you do?

                                  A) Integrate with a single sign-on (SSO) platform, and pass each user's credentials along with the query request
                                  B) Create a service account and grant dataset access to that accoun
                                  C) Create a dummy user and grant dataset access to that use
                                  D) Store the username and password for that user in a file on the files system, and use those credentials to access the BigQuery dataset
                                  E) Use the service account's private key to access the dataset
                                  F) Create groups for your users and give those groups access to the dataset


                                  4. 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) Write an Apache Beam pipeline that creates a BigQuery table per data
                                  B) 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.
                                  C) 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.
                                  D) Re-create the tables using DD
                                  E) Recommend that the Data Science team use wildcards on the table name suffixes to select the data they need.
                                  F) Partition the tables by a column containing a TIMESTAMP or DATE Type.


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


                                  Solutions:

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

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