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Cloudera CDP-3002 Exam Syllabus Topics:

Cloudera CDP Data Engineer - Certification Sample Questions:

1. Explain the concept of lineage tracking in Spark and its benefits for fault tolerance and debugging.

A) It creates a log of all operations performed on data, aiding in debugging issues.
B) Both A and B.
C) Lineage tracks the dependencies between data transformations, enabling efficient re-execution on failures.
D) Lineage allows for caching intermediate results, improving performance.

2. In the context of schema inference, which component of the Apache Spark ecosystem plays a crucial role in enabling the exploration of semi-structured data?

A) DataFrame API
B) Spark Streaming
C) Spark SQL
D) RDD (Resilient Distributed Dataset)

3. You notice degraded read performance on an Iceberg table after many updates and deletes. What maintenance task should you perform to improve this?

A) Compact data files
B) Rewrite manifest files
C) Rebuild the Iceberg metadata table
D) Delete old snapshots

4. You need to design an Airflow DAG that waits for a specific file to become available before proceeding with the downstream tasks. How can you achieve this dependency?

A) Implement a custom loop within a Python operator to continuously check for the file until it appears.
B) Use the File sensor operator to check for the file's existence and trigger downstream tasks upon its arrival.
C) Configure the source system to notify Airflow when the file is ready for processing.
D) Schedule the DAG to run periodically, hoping the file becomes available eventually.

5. What is the difference between RDDs and DataFrames in Spark?

A) RDDs represent distributed datasets, while DataFrames work with structured data
B) RDDs are lower-level abstractions, while DataFrames offer higher-level functionalities
C) RDDs are mutable, while DataFrames are immutable
D) Both B and C

Solutions: