Updated: Jul 28, 2026
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| Certification Vendor: | Cloudera |
| Exam Name: | CDP Data Engineer - Certification Exam |
| Exam Number: | CDP-3002 |
| Certificate Validity Period: | 2 years |
| Related Certifications: | Cloudera Certified Associate (CCA) Data Analyst Cloudera Certified Professional (CCP) Data Engineer |
| Available Languages: | English |
| Exam Format: | Multiple Choice, Hands-on Lab (Performance-based) |
| Exam Duration: | 120 minutes |
| Passing Score: | 70% |
| Exam Price: | USD 295 |
| Real Exam Qty: | 60-70 |
| Sample Questions: | Cloudera CDP-3002 Sample Questions |
| Exam Way: | Online proctored exam at authorized testing centers |
| Pre Condition: | Recommended: Hands-on experience with Cloudera CDP, familiarity with Python/Scala, and understanding of distributed data processing concepts |
| Official Syllabus URL: | https://www.cloudera.com/about/certification/cdp-certification.html |
| Section | Weight | Objectives |
|---|---|---|
| CDP Platform Operations | 15% | - Cluster Management and Monitoring - Cloudera Data Platform Architecture - Cloudera Flow Management - Data Lake and Storage |
| Data Processing with Spark | 30% | - Spark Structured Streaming - Spark Core Concepts - Spark SQL and DataFrames - DataFrame and Dataset APIs - Spark Performance Optimization |
| Data Pipeline Orchestration | 20% | - Workflow Dependencies - Pipeline Scheduling and Triggers - Error Handling and Retries - Apache Airflow on CDP |
| Data Ingestion and Integration | 20% | - Data Federation - Batch Data Ingestion - Stream Data Ingestion - Data Transformation and ETL - CDC (Change Data Capture) |
| Data Quality and Governance | 15% | - Access Control and Security - Data Catalog and Metadata - Data Validation and Cleansing - Data Lineage |
1. A data analyst is performing a join operation in PySpark between a large DataFrame Clarge_df) and a much smaller DataFrame ('small_df). The process is very slow. What can the analyst do to optimize this join operation?
A) Increase the number of partitions in 'small_df'.
B) Use "key").
C) Utilize the 'broadcast' function in the join operation.
D) Apply before the join.
2. In Airflow, what is a Hook used for?
A) To define the execution order of tasks
B) To connect to external systems and databases
C) To retry failed tasks automatically
D) To visualize task execution and logs
3. An Airflow DAG designed to run a sequence of data validation checks generates a dynamic number of validation tasks based on the incoming data's characteristics. Each validation task must complete successfully before a final data processing task can begin. Which Airflow feature is most suitable for implementing this pattern?
A) SubDAGs
B) The TriggerDagRunOperator
C) A BranchPythonOperator with a follow-up Join task
D) The Dynamic Task Mapping feature
4. You are deploying a Spark application on Kubernetes and need to specify the amount of memory allocated to each Executor. In your PySpark code, which configuration setting will you use?
A) 'spark.driver.memory'
B) 'spark.executor.instances'
C) 'spark.executor.memoryoverhead'
D) 'spark.executor.memory'
5. You're implementing a data quality process for Iceberg tables in CDP Which of the following Iceberg features can help you enforce constraints and detect data anomalies? (Choose two)
A) Iceberg table constraints (not yet fully supporteD.
B) Manifest files
C) Partitioning
D) Snapshots
E) Metadata tables
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: B | Question # 3 Answer: D | Question # 4 Answer: D | Question # 5 Answer: B,E |
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