Professional Data Engineer on Google Cloud Platform
Professional Data Engineer
Money Back Guarantee
98% Success Rate
Real Questions
2
Ingesting and processing data
3
Storing data
4
Preparing and using data for analysis
5
What topics are on the PROFESSIONAL-DATA-ENGINEER exam?
How do I earn the Professional Data Engineer on Google Cloud Platform certification?
Official Pathway Guidance
Track: Data Engineering
Prerequisites
Next Steps
- Cloud-FellowGoogle Cloud Fellow (by nomination)
Current Level
Alternative Paths
- Professional-Workspace-AdministratorGoogle Workspace Administrator
Career Progression
Entry Level
- Junior Data Engineer
- Data Analyst
Mid Level
- Data Engineer
- Cloud Data Engineer
- ETL Developer
Senior Level
- Senior Data Engineer
- Lead Data Engineer
- Data Architect
Executive Level
- Principal Data Engineer
- Chief Data Officer
- Head of Data Engineering
Complementary Skills
Technical
Python programmingSQL masteryApache BeamApache SparkTerraform/Infrastructure as CodeDocker/Kubernetes
Soft Skills
Problem-solvingCommunication with stakeholdersProject managementCost optimization mindset
Certification Maintenance
- Recertification Options:Retake the current exam versionPass any other Professional level certificationComplete continuous learning paths on Cloud Skills Boost
How do I study for the PROFESSIONAL-DATA-ENGINEER Exam?
Practice the Professional Data Engineer on Google Cloud Platform with our Exam Simulator
Free practice questions, exam guides, and real exam‑style explanations in our Planet Cert simulator.
Official Resources
Official Documentation
BigQuery Documentationproduct_docsDataflow Documentationproduct_docsPub/Sub Documentationproduct_docsDataproc Documentationproduct_docsCommunity Resources
Google Cloud Communityofficial_forumReddit r/googlecloudcommunityGoogle Cloud Blog - Data AnalyticsofficialMedium - Google Cloud CommunitycommunityHands-on Labs
Google Cloud Free TierBigQuery SandboxStudy Tips
- Focus on BigQuery optimization techniques
- Practice with streaming and batch pipeline scenarios
- Understand cost optimization strategies
- Learn the new services introduced in 2023-2024
- Focus less on ML (reduced in new exam version)
- Master IAM and security best practices
- Practice troubleshooting data pipeline issues
What's changed on this exam?
Technology Coverage
DataplexGA
Now heavily featured in exam questions
Released: 2023-06-01AlloyDBGA
Appears in database selection scenarios
Released: 2023-05-01DatastreamGA
Critical for CDC and migration questions
Released: 2023-01-01BigQuery MLExtended
Reduced focus but still present
Released: 2024-03-01Industry Trends
- AI/ML integration becoming standard in data engineering tools
- Increased focus on real-time streaming over batch processing
- Data mesh and decentralized data architectures gaining traction
- Cost optimization becoming critical skill
- Zero-trust security model for data access
- Multi-cloud and hybrid cloud data strategies
Who should take this exam?
Recommended Experience
- 3+ years of industry experience
- 1+ years designing and managing solutions using Google Cloud
- Experience with data processing systems and machine learning models
- Familiarity with Google Cloud data and ML products
Target Audience
- Data Engineers
- Data Architects
- Data Scientists transitioning to engineering
- Cloud Architects focusing on data
- Backend Engineers working with data pipelines
Experience Level: Professional
How do I register & what's the exam fee?
| Exam Cost | $200 USD |
| Testing Centers | Pearson VUE, Kryterion |
| Online Proctoring | Available |
| Retake Policy | First Attempt Wait: 14 days Second Attempt Wait: 60 days Third Attempt Wait: 365 days |
How long is the certification valid?
| Valid For | 2 years |
| Recertification |
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