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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q13-Q18):

NEW QUESTION # 13
How does the Cloudera Data Engineering service integrate with cloud storage solutions like Amazon S3 or Azure Blob Storage?

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

Answer: C

Explanation:
The Cloudera Data Engineering service comes with pre-built connectors for various cloud storage solutions like Amazon S3 and Azure Blob Storage. These connectors simplify data movement and integration between the platform and cloud storage solutions.

NEW QUESTION # 14
Why are partitioned tables beneficial in Hive for large datasets?

A. They automatically encrypt data based on the partition key.
B. They ensure that all data is stored in a single file, simplifying access.
C. They allow for faster query processing by scanning only relevant partitions.
D. They decrease the security of the data.

Answer: C

Explanation:
Partitioned tables are beneficial because they allow Hive to perform faster query processing by scanning only the relevant partitions of data rather than the entire dataset. This approach significantly reduces the amount of data read from disk during query execution, improving performance, especially with large datasets.

NEW QUESTION # 15
What is the role of a Spark driver in a distributed processing job?

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

Answer: C

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

NEW QUESTION # 16
Your team is integrating PySpark with a MySQL database. You need to read data from a table named 'employees'. Which of the following PySpark code snippets correctly accomplishes this task?

Answer: D

Explanation:
Option A is correct because it properly uses the JDBC format with all the necessary options including the URL, database table, and user credentials.

NEW QUESTION # 17
In the context of packaging a PySpark application, what is the purpose of the 'requirements.txt' file?

A. To define the Spark version compatible with the application.
B. To list all the third-party dependencies required by the application.
C. To specify the Python version required for the application.
D. To list the environment variables needed for the application.

Answer: B

Explanation:
The 'requirements.txt' file is used to list all third-party libraries (dependencies) that the PySpark application needs. These dependencies are then installed using pip.

NEW QUESTION # 18
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