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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q191-Q196):

NEW QUESTION # 191
You are processing a large dataset using Spark and need to ensure that the results are available for subsequent stages without recomputing. Which approach achieves this efficiently?

A. Store the data in a temporary table using Spark SQL
B. Use rdd.persist() with the appropriate storage level based on your needs
C. Leverage Spark's automatic checkpointing mechanism
D. Implement custom logic to save the data to HDFS between stages

Answer: B

Explanation:
While other options might work, rdd.persist() is the recommended approach for Spark's distributed persistence. It allows you to specify the storage level (e.g., MEMORY_ONLY, MEMORY_AND_DISK) for intermediate RDDs, ensuring they are available for future stages without recomputing, improving efficiency and performance.

NEW QUESTION # 192
You're building an Airflow DAG that consists of multiple interdependent ETL pipelines. How can you ensure they execute in the correct order and avoid conflicts?

A. Implement a custom script to manage the execution order of the pipelines.
B. Schedule each pipeline separately with appropriate scheduling intervals.
C. Run all pipelines simultaneously, assuming they are independent.
D. Utilize Airflow sub-DAGs to group related tasks and define dependencies between them.

Answer: D

Explanation:
Airflow sub-DAGs provide a structured way to organize and manage complex workflows. Option B allows you to group related ETL pipelines into sub-DAGs and define dependencies between them, ensuring they execute in the desired order while preventing potential conflicts.

NEW QUESTION # 193
Why are partitioned tables beneficial in Hive for large datasets?

A. They ensure that all data is stored in a single file, simplifying access.
B. They decrease the security of the data.
C. They automatically encrypt data based on the partition key.
D. They allow for faster query processing by scanning only relevant partitions.

Answer: D

Explanation:
Partitioned tables are beneficial because they allow Hive to perform faster query processing by scanning only the relevant partitions of data rather than the entire dataset. This approach significantly reduces the amount of data read from disk during query execution, improving performance, especially with large datasets.

NEW QUESTION # 194
You encounter an error message stating "Failed to find persisted data for RDD" in your Spark application. What are the potential causes and how can you troubleshoot them?

A. All of the above
B. Spark encountered a network issue and couldn't access the persisted data
C. The data might have been corrupted or deleted from the storage location
D. The RDD was never persisted, or the storage level was set incorrectly

Answer: A

Explanation:
The error message indicates missing persisted data. Potential causes include A not persisting the RDD or using an inadequate storage level, B network issues preventing data access, or C data corruption/deletion. Examining the application logic, storage configuration, and Spark logs can help identify the specific cause and take corrective actions.

NEW QUESTION # 195
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 distribute data evenly across the cluster
C. To minimize the amount of data processed
D. To choose the data storage format

Answer: A

Explanation:
The Cost-Based Optimizer (CBO) aims to select the most efficient execution plan for a query based on the cost associated with various operations. It considers factors like data size, data locality, and the computational cost of operations to optimize the query execution.

NEW QUESTION # 196
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