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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q280-Q285):

NEW QUESTION # 280
In the context of Spark, what is a potential downside of indiscriminate use of data caching, especially with the MEMORY_AND DISK storage level?

A. It can lead to reduced fault tolerance due to reliance on in-memory storage.
B. It may increase execution time due to overheads from frequent disk 1/0 operations.
C. It can decrease network traffic by reducing the need for data shuffling.
D. It enhances data security by storing intermediate results in encrypted form.

Answer: B

Explanation:
Indiscriminate caching, especially with the MEMORY_AND DISK storage level, can lead to increased execution time due to the overheads associated with frequent disk I/O operations. When the memory capacity is exceeded, data is spilled to disk, which can significantly slow down data access compared to in-memory operations. While this approach ensures that the data is not lost if it exceeds memory capacity, it introduces additional latency due to disk access times.

NEW QUESTION # 281
How does the Cloudera Optimization Framework leverage columnar storage formats for query performance improvement?

A. By enabling more efficient compression and encoding schemes
B. By reducing network traffic through columnar data replication
C. By encrypting data stored in columnar formats
D. By allowing more efficient data scans and operations on compressed data

Answer: D

Explanation:
Columnar storage formats allow for more efficient data scans and operations, especially for queries that access a subset of columns in a large dataset This efficiency comes from the ability to read, compress, and process only the necessary columns for a query, significantly reducing I/O and CPU usage compared to row-based storage.

NEW QUESTION # 282
You need to design a DAG that can be easily scaled horizontally to handle increasing workload. How can you achieve this goal?

A. Leverage Airflow pools to manage resource allocation and limit concurrent task execution.
B. Utilize external scheduling tools like Cron to manage DAG execution independently of Airflow workers.
C. Implement custom logic within each task to handle parallel processing independently.
D. Increase the number of worker processes in the Airflow web server configuration.

Answer: A,D

NEW QUESTION # 283
You want to write the results of a Spark DataFrame back to a Hive table. How can you achieve this efficiently?

A. Use the DataFrame.write.saveAsTable("table_name") method
B. Use the hiveContext.sql("lNSERT INTO table_name SELECT FROM df') method
C. Implement a custom function to write individual rows to the Hive table
D. Convert the DataFrame to a temporary table and then use HiveQL operations

Answer: A

Explanation:
Option B provides a concise and efficient way to write Spark DataFrame content to a Hive table. This method automatically handles schema conversion and data partitioning, offering a seamless integration between the two frameworks.

NEW QUESTION # 284
In a PySpark application running on Kubernetes, you want to enable dynamic allocation of Executors. Which configuration setting is essential to turn on this feature?

A. 'spark.kubernetes.executor.dynamicAllocation'
B. 'spark.dynamicAllocation.enabled'
C. 'spark.kubernetes.dynamicAllocation.enabled'
D. 'spark.executor.instances'

Answer: B

Explanation:
The configuration 'spark.dynamicAllocation.enabled' is used to enable the dynamic allocation feature in Spark applications. This feature allows Spark to dynamically adjust the number of Executor pods in Kubernetes based on the current workload.

NEW QUESTION # 285
......

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