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Google Cloud Professional Data Engineer Syllabus

Designing data processing systems

Designing data systems for security, compliance, reliability, portability and migration.

22%
Weight
11
Questions
22
Marks

Security and Compliance

  • IAM and organization policies
  • Encryption and key management
  • PII protection strategies
  • Regional data sovereignty
  • Project, dataset and table design for governance

Reliability, Fidelity and Portability

  • Data cleaning with Dataform, Dataflow and Cloud Data Fusion
  • Pipeline monitoring and orchestration
  • Disaster recovery and fault tolerance
  • ACID and availability trade-offs
  • Multi-cloud and data residency portability

Data Migrations

  • BigQuery Data Transfer Service
  • Database Migration Service
  • Datastream
  • Transfer Appliance
  • Validating migrated data

Ingesting and processing the data

Planning, building and operationalizing batch and streaming pipelines.

25%
Weight
13
Questions
25
Marks

Planning Pipelines

  • Data sources and sinks
  • Transformation and orchestration logic
  • Networking fundamentals
  • Data encryption in pipelines

Building Pipelines

  • Dataflow and Apache Beam
  • Dataproc, Spark and the Hadoop ecosystem
  • Pub/Sub and Apache Kafka
  • Windowing and late-arriving data
  • AI data enrichment

Deploying and Operationalizing

  • Cloud Composer orchestration
  • Workflows
  • Job automation
  • CI/CD for data pipelines

Storing the data

Selecting storage systems and designing data warehouses, data lakes and data platforms.

20%
Weight
10
Questions
20
Marks

Selecting Storage Systems

  • BigQuery and BigLake
  • Bigtable, Spanner and AlloyDB
  • Cloud SQL, Firestore and Memorystore
  • Cloud Storage lifecycle management
  • Matching access patterns, cost and performance

Warehouses and Lakes

  • Data modeling and normalization
  • Warehouse architecture for access patterns
  • Data lake discovery, access and cost controls
  • Data lake monitoring

Data Platform Design

  • Dataplex and Dataplex Catalog
  • BigQuery and Cloud Storage as a platform
  • Federated governance
  • Data discovery and cataloging

Preparing and using data for analysis

Preparing data for visualization and AI/ML and sharing data securely.

15%
Weight
7
Questions
15
Marks

Visualization and Query Performance

  • BI Engine and materialized views
  • Precalculated fields
  • Troubleshooting slow queries
  • Data masking, IAM and Cloud DLP

Data for AI and Sharing

  • Feature engineering and serving with BigQuery ML
  • Preparing unstructured data for embeddings and RAG
  • Analytics Hub (BigQuery sharing)
  • Publishing datasets and reports

Maintaining and automating data workloads

Optimizing resources, automating jobs, managing capacity, monitoring and mitigating failures.

18%
Weight
9
Questions
18
Marks

Resources and Automation

  • Minimizing cost per business need
  • Persistent vs. job-based Dataproc clusters
  • Cloud Composer DAGs and scheduling
  • BigQuery Editions and reservations
  • Interactive vs. batch queries

Monitoring and Failure Handling

  • Cloud Monitoring, Cloud Logging and the BigQuery admin panel
  • Troubleshooting errors, billing and quotas
  • Fault tolerance and job restarts
  • Multi-region and multi-zone execution
  • Replication and failover for Cloud SQL and Redis

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Learning Progress

Designing data processing systems 85%
Ingesting and processing the data 92%

Practice Test Scores

95%
Latest Score
Above passing threshold

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