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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q200-Q205):

NEW QUESTION # 200
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. Use Spark's broadcast variables for frequently accessed data across iterations
B. Store intermediate results in temporary tables using Spark SQL
C. Leverage Spark's in-memory caching capabilities with rdd.cache()
D. Implement custom data structures for managing intermediate data

Answer: C

Explanation:
Caching intermediate results with rdd.cache() B provides efficient access and updates between iterations, as data resides in memory, minimizing disk 1/0. While temporary tables A and custom data structures C might work, they can be less efficient for iterative processing. Broadcast variables D are suitable for frequently accessed data across all iterations, not specifically for intermediate results.

NEW QUESTION # 201
You need to optimize the performance of a Spark query that involves joining data from multiple Hive tables. What strategies can you employ to improve efficiency?

A. Use broadcast joins for small tables involved in the join operation
B. Pre-partition tables based on the join columns for faster data co-location
C. Increase the number of Spark executors without any further optimization
D. All of the above

Answer: D

Explanation:
A combination of strategies can significantly improve join performance. Increasing executors A can help, but it's not the sole solution. Broadcasting small tables B minimizes data movement. Pre-partitioning C ensures relevant data is on the same nodes, reducing network overhead. Combining these techniques provides the most optimal approach.

NEW QUESTION # 202
You want to select specific columns from a Spark DataFrame and rename them. How can you achieve this in Spark SQL?

A. Use the select() method with column names and aliases within parentheses
B. Implement custom logic to iterate through the DataFrame and create a new one
C. Use Spark SQL's ALTER TABLE statement to modify the table schema
D. Modify the original DataFrame schema directly

Answer: A

Explanation:
Modifying the original schema B is not recommended as it can affect subsequent operations. Custom logic C is inefficient. Spark SQL's select() method A allows you to specify the columns and assign aliases for renaming, providing a concise and efficient way to achieve the desired outcome.

NEW QUESTION # 203
How do bucketed tables in Hive improve query performance?

A. By organizing data into fixed-size segments, or buckets, based on a hash function of a column
B. By encrypting data to reduce 1/0 operations
C. By compressing data automatically to reduce storage space
D. By replicating data across multiple nodes to improve access speed

Answer: A

Explanation:
Bucketed tables in Hive improve query performance by organizing data into fixed-size segments, or buckets, based on a hash function of a column. This organization enables more efficient data access for operations like joins and aggregations by reducing the amount of data that needs to be scanned and allowing for more efficient distribution of data across the cluster.

NEW QUESTION # 204
What is the role of a Spark driver in a distributed processing job?

A. Performs computations on individual data partitions
B. Stores and processes intermediate data
C. Manages communication between executors and workers
D. Coordinates tasks across the cluster

Answer: D

Explanation:
The driver program acts as the central entity, submitting jobs, scheduling tasks on executors, and managing dependencies between stages

NEW QUESTION # 205
......

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Cloudera CDP-3002 Latest Exam Review - CDP-3002 Actual Dump

CDP-3002 Actual Dump & CDP-3002 Valid Test Testking

Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q200-Q205):

Pass Guaranteed 2026 Cloudera Perfect CDP-3002: CDP Data Engineer - Certification Exam Latest Exam Review

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