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Cloudera CDP-3002 Exam Syllabus Topics:

Cloudera CDP Data Engineer - Certification Sample Questions:

1. Your Airflow DAG performs data quality checks that involve complex data transformations and aggregations. How can you ensure these checks are executed efficiently and don't impact the performance of the entire pipeline?

A) Utilize libraries like Pandas or Spark for data manipulation within the PythonOperator, leveraging optimization features where available.
B) Offload complex data processing tasks to separate DAGs designed specifically for data preparation.
C) Use Airflow's built-in data manipulation operators like SQLExecuteOperator for basic checks, avoiding complex transformations.
D) Implement the data transformations and aggregations directly within the checking logic, regardless of efficiency.

2. In Apache Spark, which of the following is the most effective strategy for minimizing data shuffling across nodes in a cluster?

A) Using broadcast variables for small data
B) Decreasing the number of partitions
C) Filtering data after a wide transformation
D) Increasing the number of partitions

3. You're working with a complex data pipeline involving both Spark and Hive operations. How can you ensure data consistency and avoid data corruption across different stages?

A) Use separate clusters for Spark and Hive processing
B) Rely solely on Spark's checkpointing capabilities
C) Leverage ACID transactions in both Spark and Hive
D) Manually manage data consistency through custom code

4. What is the purpose of partitioning data in Spark?

A) To enforce data access control
B) To optimize data visualization
C) To enable parallel processing across multiple nodes
D) To improve data compression efficiency

5. When creating a data pipeline in the Cloudera Data Engineering service, what is the primary file format used to define the pipeline steps and configuration?

A) Shell script
B) Python script
C) JSON
D) YAML

Solutions: