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Cloudera CDP Data Engineer - Certification Sample Questions:

1. In Apache Spark, which storage level is recommended for caching data that is accessed frequently but is too large to fit in memory?

A) DISK ONLY
B) OFF HEAP
C) MEMORY ONLY
D) MEMORY AND DISK

2. Your Airflow DAG includes tasks that can potentially fail due to various reasons. How can you handle such failures and ensure the overall workflow continues as intended?

A) Implement custom logic within each task to handle potential errors and retry failed tasks manually.
B) Utilize Airflow XCom to share information about failed tasks with downstream tasks for alternative processing.
C) All of the above
D) Configure the DAG to automatically retry failed tasks a specific number of times.

3. 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) Use a broadcast variable for small, frequently accessed data
B) All of the above
C) Increase the number of executors in your cluster
D) Reduce the number of partitions in your RDDs

4. In the context of Hive, what mechanism ensures that data is evenly distributed across buckets?

A) External data balancing tools
B) Natural key distribution
C) A hash function applied to the bucketing column
D) Manual data insertion scripts

5. What is a primary benefit of schema-on-read compared to schema-on-write in big data processing frameworks?

A) It mandates manual intervention for each data read operation.
B) It requires all data to conform to a rigid schema before storage.
C) It significantly reduces the computational overhead during data ingestion.
D) It allows for greater flexibility in data formats and structures at the time of analysis.

Solutions: