Cloudera CDP-3002 - CDP Data Engineer - Certification Exam

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Cloudera CDP-3002 Exam Syllabus Topics:

Cloudera CDP Data Engineer - Certification Sample Questions:

Question 1

What is a primary consideration when deciding to cache data in a distributed computing environment like Apache Spark?

A. Caching every dataset regardless of its access frequency
B. Using disk storage for all cached data to improve fault tolerance
C. Ensuring data is encrypted before caching
D. The trade-off between memory usage and computational efficiency

Question 2

Which of the following statements best describes the process of schema inference in the context of big data processing?

A. Automatically determining the data structure of a dataset.
B. Assigning random data types to columns in unstructured data.
C. Manually defining the schema for each dataset before processing.
D. Encrypting data based on its type.

Question 3

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. Utilize Airflow sub-DAGs to group related tasks and define dependencies between them.
D. Run all pipelines simultaneously, assuming they are independent.

Question 4

How does the Cloudera Data Engineering service integrate with cloud storage solutions like Amazon S3 or Azure Blob Storage?

A. Utilizes built-in connectors for seamless access.
B. Not directly supported, requires external tools.
C. Requires custom scripting for each cloud storage provider.
D. Requires manual configuration for each storage bucket.

Question 5

You are working with a large dataset consisting of multiple files. How can you efficiently load the data into Spark while considering efficient storage and processing?

A. Load each file individually using spark.read.textFile("/path/to/file")
B. Use spark.read.textFile("/path/to/directory/") to read all files at once
C. Leverage partitioning techniques like spark.read.textFile("/path/to/directory").repartition(n)
D. All of the above

Solutions: