Exam facts (full table)
| Attribute | Details |
|---|---|
| Code | CDP-3002 |
| Name | CDP Data Engineer Exam |
| Vendor | Cloudera |
| Level | Professional |
| Duration | 90 minutes |
| Question Count | 50 questions |
| Question Types | Scenario-based, practical |
| Pass Score | 55% |
| Delivery | Online, proctored |
| Cost | $330 USD |
| Validity | 2 years |
| Prerequisites | Hands-on experience with Spark, Airflow, and CDP |
| Exam Resources | None allowed (no reference materials) |
Vendor source — Cloudera CDP Data Engineer ↗
About
The CDP Data Engineer certification validates professional-level expertise in designing, developing, and optimizing data workflows on the Cloudera Data Platform. This role-specific certification is designed for engineers implementing data pipelines, optimizing performance, and managing data storage on CDP.
Data engineers certified at this level demonstrate proficiency in Apache Spark, workflow orchestration with Airflow, performance tuning, security configuration, and cloud integration—essential skills for building production-grade data systems.
Domain context
Modern data engineering on CDP requires mastery across multiple dimensions:
Data Modeling & Storage: Designing efficient schemas, leveraging Apache Iceberg for open table format capabilities, choosing appropriate partitioning strategies, and optimizing storage formats for query performance.
Processing Frameworks: Deep expertise in Apache Spark for distributed data processing, from RDD fundamentals to DataFrame optimizations, and understanding Spark's execution model.
Workflow Orchestration: Using Apache Airflow to orchestrate complex, multi-step data pipelines with dependencies, error handling, and monitoring.
Performance Optimization: Identifying bottlenecks, tuning query execution, optimizing resource allocation, and understanding cluster performance metrics.
Security & Deployment: Configuring Ranger policies, managing authentication/authorization, securing sensitive data, and deploying to public cloud (AWS, Azure, GCP) and on-premises environments.
Topics covered
Apache Spark (48% of exam weight)
- Spark architecture and execution model
- RDD vs. DataFrame vs. Dataset APIs
- Transformations and actions
- Optimization techniques and Catalyst optimizer
- Spark SQL and Hive integration
- Structured streaming
- Performance tuning and partitioning strategies
Performance Tuning (22% of exam weight)
- Identifying performance bottlenecks
- Query optimization
- Resource allocation and YARN configuration
- Caching and persistence strategies
- Shuffle optimization
- Broadcast variables and accumulators
- Spark listener and monitoring
Apache Iceberg (10% of exam weight)
- Iceberg table format advantages
- Schema evolution and hidden partitioning
- Time travel and rollback capabilities
- ACID transactions on data lakes
- Iceberg in Cloudera ecosystem
Apache Airflow (10% of exam weight)
- Airflow architecture and components
- DAG definition and scheduling
- Operators and sensors
- Error handling and retries
- Monitoring and alerting
- Task dependencies and workflow patterns
Deployment (10% of exam weight)
- CDP deployment architectures
- Public cloud deployment (AWS, Azure, GCP)
- Networking and storage configuration
- Cluster provisioning and scaling
- High availability and disaster recovery
Security and Monitoring
- Ranger policy administration
- Data encryption and masking
- Audit logging
- Cluster monitoring and troubleshooting
Common job-ready skills
- Design and implement scalable data pipelines using Spark
- Optimize data processing for performance and cost
- Implement workflow automation with Airflow
- Design efficient data models with Iceberg
- Configure security policies and manage access control
- Deploy and manage CDP clusters on public cloud
- Monitor and troubleshoot data pipeline issues
- Handle streaming data and real-time processing
- Implement data quality checks and error handling
- Estimate resource requirements and cost optimization
Recommended courses
- Cloudera University: CDP Data Engineer Official Training
- Udemy: "Cloudera Data Engineer Certification CDP-3002 2026"
- Linux Academy: Apache Spark and Data Engineering path
- Pluralsight: Spark and Airflow specialization
- A Cloud Guru: Spark Fundamentals and Advanced
Practice exams
- AnalyticsExam: CDP-3002 Data Engineer simulator
- Udemy: CDP-3002 Simulator Test with 300+ questions
- QuickTechie: CDP-3002 practice questions and Q&A
- BigDataRise: Exam deep dive with practice questions
Books
- "Learning Spark" by Jules S. Damji et al. (Spark fundamentals)
- "High Performance Spark" by Rachel Warren and Holden Karau
- "Fundamentals of Apache Airflow" by Bas P. Harenslak
- Official Cloudera documentation and exam guides
Job titles
- Data Engineer
- Big Data Engineer
- Cloud Data Engineer
- ETL Developer
- Data Pipeline Engineer
- Spark Developer
- Data Integration Engineer
- Analytics Engineer
- Cloud Solutions Architect (Data)
- Senior Data Engineer
Salary (USD / ZAR×18 / GBP / EUR / AUD)
| Region | Low | Mid | High |
|---|---|---|---|
| USD | $110,000 | $155,000 | $210,000 |
| ZAR×18 | R1,980,000 | R2,790,000 | R3,780,000 |
| GBP | £85,000 | £120,000 | £160,000 |
| EUR | €95,000 | €135,000 | €180,000 |
| AUD | A$160,000 | A$230,000 | A$310,000 |
Note: Professional data engineer roles typically command 30-50% premium over associate levels. Cloud expertise adds 10-20% additional premium.
Skills validated
- Apache Spark architecture and optimization
- Distributed data processing design patterns
- Data pipeline development and orchestration
- Performance bottleneck identification and tuning
- Data storage optimization (Iceberg, HDFS, Ozone)
- Security policy implementation
- Cloud deployment architecture
- Workflow automation and error handling
- Monitoring and troubleshooting expertise
Related certs
- Cloudera CDP-0011: Generalist (foundational, complementary)
- Cloudera CDP-5001: Administrator – Public Cloud (operations-focused)
- Cloudera CDP-6001: Machine Learning Engineer (ML-specific)
- Apache Spark Certification: Databricks (alternative path)