GOOGLE CLOUDGoogle Cloud Professional Data Engineer
The Google Cloud Professional Data Engineer certification validates your ability to design, build, and operationalize data processing systems on Google Cloud. It is for data engineers who architect scalable, reliable, and secure data solutions that turn raw data into actionable insights. Earning it demonstrates you can apply Google Cloud technologies to solve real-world data challenges and drive business value.
457 practice questions · Updated 2026-07-30
5Domains
19Objectives
141Concepts
457Questions
PROFESSIONAL-DATA-ENGINEER Curriculum
Every domain, objective, and concept the PROFESSIONAL-DATA-ENGINEER exam measures.
- Cloud IAM basics
- Organization policies
- Encryption at rest and in transit
- Key management
- Handling PII
- Data sovereignty
- Regulatory compliance
- Data governance architecture
- Multi-environment design
- Data preparation and cleaning with Dataform
- Data preparation and cleaning with Dataflow
- Data preparation and cleaning with Cloud Data Fusion
- Prompting LLMs for query generation
- Monitoring data pipelines
- Orchestration of data pipelines
- Disaster recovery planning
- Fault tolerance in data processing
- ACID compliance in data systems
- Availability vs. consistency trade-offs
- Data validation techniques
- Requirements mapping
- Architecture flexibility
- Portability design
- Data residency compliance
- Data staging
- Data cataloging
- Data profiling
- Data discovery
- Stakeholder and requirements analysis
- Current state assessment
- Desired state definition
- Migration planning and gap analysis
- Migration strategy selection
- Data migration services overview
- BigQuery Data Transfer Service
- Database Migration Service
- Transfer Appliance
- Datastream
- Google Cloud networking for migration
- Migration validation and testing
- Identify data sources
- Identify data sinks
- Define data transformation logic
- Define orchestration logic
- Apply networking fundamentals
- Implement data encryption
- Data cleansing techniques
- Dataflow and Apache Beam
- Dataproc and Hadoop ecosystem
- Cloud Data Fusion
- BigQuery for data processing
- Pub/Sub for streaming ingestion
- Apache Spark and Kafka
- Data transformations
- Data acquisition and import
- Integrating new data sources
- Cloud Composer basics
- DAG authoring in Cloud Composer
- Cloud Composer operations
- Workflows service
- Comparing Composer and Workflows
- CI/CD principles for data pipelines
- CI/CD tooling on Google Cloud
- Pipeline deployment automation
- Testing and validation in CI/CD
- Data access pattern analysis
- Managed service selection criteria
- BigQuery and BigLake use cases
- AlloyDB and Cloud SQL use cases
- Bigtable and Spanner use cases
- Cloud Storage and Firestore use cases
- Memorystore use cases
- Storage cost estimation
- Performance planning for storage
- Data lifecycle management
- Storage class selection and transitions
- Data retention and deletion policies
- Data modeling fundamentals
- Designing the data model
- Normalization levels
- Choosing normalization degree
- Mapping business requirements
- Data access patterns
- Architecture for access patterns
- Data lake configuration
- Data processing in data lake
- Data lake monitoring
- Dataplex overview
- Dataplex Catalog
- Data platform design with BigQuery and Cloud Storage
- Federated governance model
- Policy management and data access control
- Data lineage and metadata management
- Connecting to visualization tools
- Precalculating fields for visualization
- BigQuery BI Engine
- Materialized views in BigQuery
- Troubleshooting poor performing queries
- Security and IAM for data access
- Data masking and Cloud DLP
- Feature engineering for ML models
- BigQuery ML data preparation
- Serving data for ML predictions
- Embedding generation for unstructured data
- Retrieval-augmented generation (RAG) data preparation
- Data sharing rules
- Publishing datasets
- Publishing reports and visualizations
- BigQuery Analytics Hub
- Cost optimization strategies for data workloads
- Resource allocation for business-critical processes
- Persistent vs. job-based clusters
- Autoscaling and cluster sizing
- Lifecycle management of clusters
- DAG fundamentals
- DAG authoring in Cloud Composer
- Scheduling DAGs
- Orchestration best practices
- BigQuery Editions
- BigQuery Reservations
- Slot capacity management
- Interactive query jobs
- Batch query jobs
- Choosing between interactive and batch
- Cloud Monitoring for data processes
- Cloud Logging for data processes
- BigQuery admin panel
- Monitoring planned usage
- Troubleshooting error messages
- Troubleshooting billing issues
- Troubleshooting quotas
- Managing jobs and queries
- Managing compute capacity with reservations
- Fault tolerance design principles
- Managing restarts and recovery
- Multi-region and multi-zone job deployment
- Handling data corruption and missing data
- Data replication strategies
- Failover mechanisms
- Cloud SQL high availability and failover
- Redis cluster replication and failover
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