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Cloudera CDP-3002 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Quality and Governance | 15% | - Access Control and Security - Data Validation and Cleansing - Data Lineage - Data Catalog and Metadata |
| Data Pipeline Orchestration | 20% | - Workflow Dependencies - Error Handling and Retries - Apache Airflow on CDP - Pipeline Scheduling and Triggers |
| Data Processing with Spark | 30% | - Spark Performance Optimization - Spark Core Concepts - DataFrame and Dataset APIs - Spark Structured Streaming - Spark SQL and DataFrames |
| Data Ingestion and Integration | 20% | - Data Federation - CDC (Change Data Capture) - Batch Data Ingestion - Stream Data Ingestion - Data Transformation and ETL |
| CDP Platform Operations | 15% | - Cloudera Flow Management - Cluster Management and Monitoring - Data Lake and Storage - Cloudera Data Platform Architecture |
Cloudera CDP Data Engineer - Certification Sample Questions:
What command and parameters should be used to update an existing Spark job in the Cloudera Data Engineering (CDE. service to increase its executor memory using the CDE CLI?
- A. cde job update -name my-spark-job --conf spark.executor.memory=6g
- B. cde spark submit -update -name my-spark-job -executor-memory 6G
- C. cde job run -edit my-spark-job -conf spark.executor.memory=6g
- D. cde job config -name my-spark-job -set spark.executor.memory=6g
Correct Answer: A 🗳️
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Which Spark configuration parameter should be increased to improve performance when dealing with large broadcast variables?
- A. spark.broadcast.blockSize
- B. spark.executor.memory
- C. spark.driver.memory
- D. spark.default.parallelism
Correct Answer: C 🗳️
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Which command line tool is essential for interacting with Cloudera's Hadoop ecosystem for file operations?
- A. Hadoop fs
- B. ci-JRL
- C. SSH
- D. Git
Correct Answer: A 🗳️
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You're building a Spark application that involves complex iterative data processing. Which option allows you to efficiently access and update intermediate results between iterations?
- A. Implement custom data structures for managing intermediate data
- B. Leverage Spark's in-memory caching capabilities with rdd.cache()
- C. Use Spark's broadcast variables for frequently accessed data across iterations
- D. Store intermediate results in temporary tables using Spark SQL
Correct Answer: B 🗳️
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What happens when a task in Airflow is marked as "skipped"?
- A. The entire DAG is marked as failed, and no further tasks are executed.
- B. Airflow retries the skipped task until it succeeds.
- C. The skipped task's downstream tasks are automatically executed.
- D. The skipped task's downstream tasks are also skipped, unless their conditions for execution are met independently.
Correct Answer: D 🗳️
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