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Google Certified Professional Data Engineer - Professional-Data-Engineer real prep

Professional-Data-Engineer
  • Exam Code: Professional-Data-Engineer
  • Exam Name: Google Certified Professional Data Engineer Exam
  • Updated: Aug 06, 2026
  • Q & A: 433 Questions and Answers
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About Google Professional-Data-Engineer: Google Certified Professional Data Engineer Exam

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Google Professional Data Engineer Practice Test Questions, Google Professional Data Engineer Exam Practice Test Questions

The Google Professional Data Engineer certification is designed to evaluate the candidates’ skills in designing data processing systems and ensuring solution quality. It is also created to measure their competence in building and operationalizing data processing systems and operationalizing ML models. The potential applicants must complete a single exam to get certified.

Reference: https://cloud.google.com/certification/data-engineer

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Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Ingesting and processing the data (~20% of the exam)20%- Deploying and operationalizing the pipelines
  • 1. CI/CD for data pipelines
  • 2. Job automation and orchestration (Cloud Composer, Workflows)
- Performing security considerations
  • 1. Identity and Access Management (IAM)
  • 2. Data encryption
  • 3. Auditing, privacy, and compliance
- Building and maintaining data structures and databases
  • 1. Defining data lifecycle
  • 2. Planning for analytical and operational use cases
Topic 2: Designing data processing systems (~30% of the exam)30%- Designing data processing resources
  • 1. Cost optimization
  • 2. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run)
  • 3. Cluster sizing and autoscaling
- Selecting appropriate storage technologies
  • 1. Choosing between BigQuery, Bigtable, Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore, AlloyDB
  • 2. Mapping storage options to business requirements
- Designing data pipelines
  • 1. AI data enrichment
  • 2. Streaming (e.g., windowing, late arriving data)
  • 3. Data acquisition and import
  • 4. Integrating with new data sources
  • 5. Batch processing
  • 6. Processing logic
Topic 3: Storing the data (~20% of the exam)20%- Selecting storage systems
  • 1. Lifecycle management of data
  • 2. Planning for storage costs and performance
  • 3. Analyzing data access patterns
- Planning for using a data warehouse
  • 1. Designing the data model
  • 2. Defining architecture to support data access patterns
  • 3. Mapping business requirements
  • 4. Deciding the degree of data normalization
- Designing for a data platform
  • 1. Building a data platform using Dataplex, Dataplex Catalog, BigQuery, Cloud Storage
  • 2. Building a federated governance model for distributed data systems
- Using a data lake
  • 1. Monitoring the data lake
  • 2. Managing the lake (data discovery, access, cost controls)
  • 3. Processing data
Topic 4: Preparing and using data for analysis (~15% of the exam)15%- Sharing data securely
  • 1. Publishing datasets
  • 2. Data sharing and collaboration
- Preparing data for visualization
  • 1. Connecting to Looker and other BI tools
  • 2. Preparing data for reporting and dashboards
Topic 5: Maintaining and automating data workloads (~15% of the exam)15%- Automating data processes
  • 1. Scheduling jobs
  • 2. Workflow orchestration
  • 3. Continuous integration and continuous deployment (CI/CD)
- Designing for reliability and fidelity
  • 1. Performing data quality and validation checks
  • 2. Planning for monitoring and alerting
  • 3. Recovering from failures
- Monitoring data pipelines and data processes
  • 1. Managing quotas and resource usage
  • 2. Logging, monitoring, and troubleshooting

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