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Cloudera CDP-3002 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Pipeline Orchestration | 20% | - Pipeline Scheduling and Triggers - Apache Airflow on CDP - Error Handling and Retries - Workflow Dependencies |
| Topic 2: CDP Platform Operations | 15% | - Cluster Management and Monitoring - Data Lake and Storage - Cloudera Flow Management - Cloudera Data Platform Architecture |
| Topic 3: Data Processing with Spark | 30% | - Spark SQL and DataFrames - DataFrame and Dataset APIs - Spark Structured Streaming - Spark Core Concepts - Spark Performance Optimization |
| Topic 4: Data Ingestion and Integration | 20% | - Data Federation - CDC (Change Data Capture) - Batch Data Ingestion - Data Transformation and ETL - Stream Data Ingestion |
| Topic 5: Data Quality and Governance | 15% | - Access Control and Security - Data Catalog and Metadata - Data Validation and Cleansing - Data Lineage |
Cloudera CDP Data Engineer - Certification Sample Questions:
1. You're facing slow performance in your Spark application due to frequent shuffles. What are some strategies you can employ to optimize shuffle operations and improve performance?
A) All of the above
B) Use a broadcast variable for small, frequently accessed data
C) Reduce the number of partitions in your RDDs
D) Increase the number of executors in your cluster
2. When deploying a packaged PySpark application using 'spark-submit', which option is used to include the packaged dependencies?
A) --jars
B) -files
C) --py-files
D) --packages
3. What is the primary purpose of the Cost-Based Optimizer (CBO) in the context of Cloudera's Optimization Framework?
A) To decide the best execution plan based on the cost of operations
B) To minimize the amount of data processed
C) To distribute data evenly across the cluster
D) To choose the data storage format
4. You're writing a Spark application that processes streaming data in real-time. How can you create DataFrames from streaming data sources?
A) All of the above
B) Leverage Spark Streaming's functions like createDataFrame with appropriate parameters
C) Utilize Spark SQL's streaming capabilities with the FROM clause
D) Use the same methods as for batch processing, reading data from a file
5. You're building an Airflow ETL pipeline that involves data validation checks. How can you integrate these checks into the pipeline and handle potential failures?
A) Leverage XCom to share the validation results with downstream tasks for further processing.
B) Use the FailoverOperator to automatically skip downstream tasks if the validation check fails.
C) Implement the validation checks within custom Python operators and raise exceptions upon failure, triggering downstream tasks to handle the error.
D) Configure the DAG to send email notifications upon validation failures for manual intervention.
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: C | Question # 3 Answer: A | Question # 4 Answer: B | Question # 5 Answer: C |






