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Exam Information
Official specifications published by Google
Exam Format
120 min
40-50
~70%
Professional
Registration
$200 USD
Kryterion (via CertMetrics) or online proctoring
English, Japanese
Validity
2 years
Retake the standard GCP-PDE exam before expiration; Take the shorter renewal exam (20 questions, 1 hour, $100) starting 60 days before expiration
GCP-PDE Exam Topics and Domains
GCP-PDE is organized into 5 weighted domains. Expect to work with BigQuery, Cloud Storage, Dataflow, Dataplex, and more.
1
Designing data processing systems
22%
Designing for security and compliance
Identity and Access ManagementData security (encryption and key management)Privacy and PII handlingRegional considerations and data sovereigntyLegal and regulatory complianceProject, dataset, and table architecture for data governanceMulti-environment use cases
- Design data systems that incorporate proper identity and access management using Cloud IAM and organization policies
- Implement data security including encryption and key management strategies
- Apply privacy strategies for handling personally identifiable information
- Configure regional considerations for data sovereignty compliance
- Ensure legal and regulatory compliance in data system design
- Design project, dataset, and table architecture for proper data governance
- Implement multi-environment architectures for development and production
Designing for reliability and fidelity
Preparing and cleaning dataMonitoring and orchestration of data pipelinesDisaster recovery and fault toleranceACID compliance and availability decisionsData validation
- Design data preparation and cleansing workflows using appropriate Google Cloud services
- Implement monitoring and orchestration for data pipelines
- Plan disaster recovery and fault tolerance for data systems
- Make informed decisions about ACID compliance and availability trade-offs
- Implement data validation strategies throughout the data lifecycle
Designing for flexibility and portability
Mapping current and future business requirements to architectureData and application portabilityData staging, cataloging, profiling, and discovery
- Design architectures that map to current and future business requirements
- Implement data and application portability strategies including multi-cloud considerations
- Configure data staging, cataloging, profiling, and discovery for effective data governance
Designing data migrations
Analyzing stakeholder needs and creating migration plansPlanning migration and validation to Google Cloud
- Analyze current stakeholder needs, users, processes, and technologies for migration planning
- Plan and validate data migrations to Google Cloud using appropriate services and strategies
2
Ingesting and processing the data
25%
Planning the data pipelines
Defining data sources and sinksDefining data transformation and orchestration logicNetworking fundamentals for data pipelinesData encryption in pipelines
- Define appropriate data sources and sinks for pipeline requirements
- Design data transformation and orchestration logic
- Configure networking fundamentals for secure data pipelines
- Implement data encryption throughout the pipeline
Building the pipelines
Data cleansingIdentifying and selecting processing servicesBatch transformationsStreaming transformationsProcessing logic and AI data enrichmentData acquisition, import, and integrating new sources
- Implement data cleansing in processing pipelines
- Select appropriate Google Cloud services for specific data processing needs
- Design and implement batch transformation pipelines
- Design and implement streaming transformation pipelines with proper windowing and late-data handling
- Incorporate AI data enrichment into processing logic
- Implement data acquisition and integration of new data sources
Deploying and operationalizing the pipelines
Job automation and orchestrationCI/CD for data pipelines
- Implement job automation and orchestration using Cloud Composer and Workflows
- Design and implement CI/CD processes for data pipelines
3
Storing the data
20%
Selecting storage systems
Analyzing data access patternsChoosing managed servicesPlanning for storage costs and performanceLifecycle management of data
- Analyze data access patterns to select appropriate storage systems
- Choose managed services that best fit specific workload requirements
- Plan for storage costs and performance optimization
- Implement lifecycle management for data across storage systems
Planning for using a data warehouse
Designing the data modelDeciding the degree of data normalizationMapping business requirements to warehouse designDefining architecture to support data access patterns
- Design data models for BigQuery data warehousing
- Make informed decisions about data normalization
- Map business requirements to data warehouse design
- Define architectures that support specific data access patterns
Using a data lake
Managing the data lakeProcessing data in the lakeMonitoring the data lake
- Manage data lakes with proper discovery, access, and cost controls
- Process data stored in data lakes using appropriate services
- Monitor data lake health, quality, and costs
Designing for a data platform
Building a data platform using Google Cloud toolsBuilding a federated governance model
- Build a data platform based on requirements using Google Cloud tools
- Design and implement a federated governance model for distributed data systems
4
Preparing and using data for analysis
15%
Preparing data for visualization
Connecting to visualization toolsPrecalculating fieldsBigQuery features for business intelligenceTroubleshooting poor performing queriesSecurity, data masking, IAM, and Cloud DLP
- Connect visualization tools to Google Cloud data sources
- Implement precalculated fields for dashboard performance
- Leverage BigQuery features for business intelligence workloads
- Troubleshoot and optimize poorly performing queries
- Implement security and data masking for analytics data
Preparing data for AI and ML
Feature engineering, training, and serving ML modelsPreparing unstructured data for embeddings and RAG
- Prepare data for feature engineering, training, and serving machine learning models
- Prepare unstructured data for embeddings and retrieval-augmented generation
Sharing data
Defining rules to share dataPublishing datasets, reports, and visualizationsBigQuery sharing via Analytics Hub
- Define rules and policies for data sharing
- Publish datasets, reports, and visualizations
- Implement BigQuery sharing using Analytics Hub
5
Maintaining and automating data workloads
18%
Optimizing resources
Minimizing costs per business needEnsuring resource availability for business-critical processesPersistent vs job-based data clusters
- Minimize costs while meeting business requirements for data workloads
- Ensure sufficient resource availability for business-critical data processes
- Choose between persistent and job-based data clusters based on requirements
Designing automation and repeatability
Creating DAGs for Cloud ComposerScheduling and orchestrating jobs
- Create directed acyclic graphs for Cloud Composer
- Schedule and orchestrate jobs in a repeatable and reliable way
Organizing workloads based on business requirements
Capacity managementInteractive or batch query jobs
- Manage capacity using BigQuery Editions and reservations
- Organize interactive and batch query workloads based on business requirements
Monitoring and troubleshooting processes
Observability of data processesMonitoring planned usageTroubleshooting error messages, billing issues, and quotasManaging workloads (jobs, queries, compute capacity)
- Implement observability for data processes using monitoring and logging tools
- Monitor planned usage and set up appropriate alerts
- Troubleshoot error messages, billing issues, and quotas
- Manage workloads including jobs, queries, and compute capacity
Maintaining awareness of failures and mitigating impact
Fault tolerance and managing restartsRunning jobs in multiple regions or zonesPreparing for data corruption and missing dataData replication and failover
- Design systems for fault tolerance and manage restarts effectively
- Deploy data workloads across multiple regions or zones for resilience
- Prepare for and recover from data corruption and missing data scenarios
- Implement data replication and failover strategies
How do I earn this certification?
Passing GCP-PDE earns the Google Cloud Certified - Professional Data Engineer certification. It sits in the Data and Analytics track.
Current Level Exams
Next Level Options
Alternative Paths
- GCP-PCSE - Professional Cloud Security EngineerDeepens security skills for data protection and compliance
- GCP-PCDO - Professional Cloud DevOps EngineerStrengthens CI/CD and operational skills for data pipelines
- GCP-PCNE - Professional Cloud Network EngineerEnhances understanding of network infrastructure underlying data systems
Practice with Precision
The PlanetCert Simulator mirrors the real exam environment with authentic questions and timed pressure.
How to study for this exam?
The most effective way to prepare for GCP-PDE is by using the PlanetCert Simulator to practice questions and review detailed explanations.
What's changed on this exam?
Current Status
ACTIVE
Updates
- Dataplex and Dataplex Catalog Prominent in exam for data governance, lake management, and federated governance
- BigLake Unified data lake and warehouse access; tested in storage and platform design sections
- Analytics Hub Data sharing and exchange; tested in analysis and sharing sections
- Dataform SQL-based transformation pipelines; tested in data preparation and reliability sections
- AlloyDB PostgreSQL-compatible managed database; included in storage service selection
- BigQuery ML and Vector Search In-database ML training and vector search for embeddings/RAG
Who should take this exam?
- 3+ years of industry experience including 1+ years designing and managing data solutions using Google Cloud