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Google Cloud Exam Syllabus

Professional Data Engineer syllabus, skills measured, and exam topics

A Professional Data Engineer empowers data-driven decisions by collecting, transforming, storing, and delivering data for diverse applications. A Professional Data Engineer designs and builds robust data infrastructure, optimizing for performance and security. This individual

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Skills measured by domain

Use the weighting table to decide where to spend the most study time.

Domain Weight
Section 1: Designing data processing systems 22%
Section 2: Ingesting and processing the data 25%
Section 3: Storing the data 20%
Section 4: Preparing and using data for analysis 15%
Section 5: Maintaining and automating data workloads 18%

Detailed outline

Scan each section as a working study checklist instead of one long wall of text.

Section 1: Designing data processing systems (~22% of the exam)

  • 1.1 Designing for security and compliance. Considerations include:
  • Identity and Access Management (e.g., Cloud IAM and organization policies)
  • Data security (encryption and key management)
  • Privacy (e.g., strategies to handle personally identifiable information)
  • Regional considerations (data sovereignty) for data access and storage
  • Legal and regulatory compliance
  • Designing the project, dataset, and table architecture to ensure proper data
  • governance
  • Multi-environment use cases (development vs. production)
  • 1.2 Designing for reliability and fidelity. Considerations include:
  • Preparing and cleaning data (e.g., Dataform, Dataflow, and Cloud Data Fusion,
  • prompting LLMs for query generation)

Section 2: Ingesting and processing the data (~25% of the exam)

  • 2.1 Planning the data pipelines. Considerations include:
  • Defining data sources and sinks
  • Defining data transformation and orchestration logic
  • Networking fundamentals
  • Data encryption
  • 2.2 Building the pipelines. Considerations include:
  • Data cleansing
  • Identifying the services (e.g., Dataflow, Apache Beam, Dataproc, Cloud Data Fusion,
  • BigQuery, Pub/Sub, Apache Spark, Hadoop ecosystem, and Apache Kafka)
  • Transformations
  • ○ Batch
  • ○ Streaming (e.g., windowing, late arriving data)

Section 3: Storing the data (~20% of the exam)

  • 3.1 Selecting storage systems. Considerations include:
  • Analyzing data access patterns
  • Choosing managed services (e.g., BigQuery, BigLake, AlloyDB, Bigtable, Spanner, Cloud
  • SQL, Cloud Storage, Firestore, Memorystore)
  • Planning for storage costs and performance
  • Lifecycle management of data
  • 3.2 Planning for using a data warehouse. Considerations include:
  • Designing the data model
  • Deciding the degree of data normalization
  • Mapping business requirements
  • Defining architecture to support data access patterns
  • 3.3 Using a data lake. Considerations include:

Section 4: Preparing and using data for analysis (~15% of the exam)

  • 4.1 Preparing data for visualization. Considerations include:
  • Connecting to tools
  • Precalculating fields
  • BigQuery features for business intelligence (e.g., BI Engine, materialized views)
  • Troubleshooting poor performing queries
  • Security, data masking, Identity and Access Management (IAM), and Cloud Data Loss
  • Prevention (Cloud DLP)
  • 4.2 Preparing data for AI and ML. Considerations include:
  • Preparing data for feature engineering, training and serving machine learning models
  • (e.g., BigQueryML)
  • Preparing unstructured data for embeddings and retrieval-augmented generation
  • 4.3 Sharing data. Considerations include:

Section 5: Maintaining and automating data workloads (~18% of the exam)

  • 5.1 Optimizing resources. Considerations include:
  • Minimizing costs per required business need for data
  • Ensuring that enough resources are available for business-critical data processes
  • Deciding between persistent or job-based data clusters (e.g., Dataproc)
  • 5.2 Designing automation and repeatability. Considerations include:
  • Creating directed acyclic graphs (DAGs) for Cloud Composer
  • Scheduling and orchestrating jobs in a repeatable way
  • 5.3 Organizing workloads based on business requirements. Considerations include:
  • Capacity management (e.g., BigQuery Editions and reservations)
  • Interactive or batch query jobs
  • 5.4 Monitoring and troubleshooting processes. Considerations include:
  • Observability of data processes (e.g., Cloud Monitoring, Cloud Logging, BigQuery