Cloudera CDP-3002

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Cloudera CDP-3002 Exam Syllabus Topics:

Cloudera CDP Data Engineer - Certification Sample Questions:

1. Which component of the Cloudera Data Engineering service is responsible for managing and scheduling data pipelines?

A) Cloudera Scheduler
B) CDH Management Console
C) Cloudera Manager
D) Cloudera Navigator

2. Discuss the trade-offs between using wide tables (many columns) and narrow tables (few columns) in Spark and the implications for data processing efficiency.

A) Spark can optimize data access patterns for both wide and narrow tables equally.
B) Both A and C.
C) Wide tables require more storage and may lead to performance overhead due to data serialization.
D) Wide tables offer better performance for filtering operations, while narrow tables excel in aggregations.

3. You are working with a large, skewed dataset in Spark. How would you optimize processing to mitigate the impact of skew and improve performance?

A) Broadcast the skewed data to all executors.
B) Addressing skewed data requires
C) Implement custom partitioners to evenly distribute skewed values.
D) Use salting on the skewed column during data partitioning.

4. Which strategy is most effective for managing schema evolution in a big data application that relies on schema inference?

A) Completely disabling schema inference and relying on static schemas
B) Using a single version of the schema for all data, regardless of changes
C) Integrating schema registry services that track and manage schema versions
D) Manually updating the inferred schema for each data ingestion

5. In the context of big data processing, what is a potential downside of relying heavily on schema inference?

A) Increased data storage efficiency
B) Reduced flexibility in handling different data types
C) Enhanced data security and compliance
D) Potential performance overhead due to the dynamic analysis of data structure

Solutions: