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CDP-3002 Valid Exam Objectives & CDP-3002 100% Exam Coverage

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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q81-Q86):

NEW QUESTION # 81
Which Airflow feature allows you to template your tasks, enabling dynamic generation of task parameters such as table names for data quality checks?

A. Variables
B. Airflow Plugins
C. XComs
D. Jinja Templating

Answer: D

Explanation:
Jinja Templating is a powerful feature in Apache Airflow that allows you to dynamically generate task parameters, including SQL queries, file paths, and other task arguments. This is especially useful for data quality checks where you might need to pass different table names or SQL queries to a task based on the execution date or other workflow parameters.

NEW QUESTION # 82
Consider a PySpark application that calculates the count of rows in a CSV file. The main application file is 'count_rows.py', and it uses a custom library 'data_utils.py'. After packaging, which 'spark-submit' command correctly submits this job including the custom library?

A. 'spark-submit --jars data utils.py count_rows.py'
B. 'spark-submit -py-files data utils.py count_rows.py'
C. 'spark-submit --files data_utils.py count_rows.py'
D. 'spark-submit --archives data_utils.py count_rows.py'

Answer: B

Explanation:
When submitting a PySpark job that relies on additional Python files (like custom libraries), the '-py-files' option is used to include these files. In this case, 'data utils.py' is a Python file needed by 'count_rows.py', so the correct command is 'spark-submit --py- files data utils.py count_rows.py'.

NEW QUESTION # 83
When deploying a Spark application on Cloudera Data Engineering (CDE. service managed on Kubernetes, which command would you use to specify resource requirements such as CPU and memory for the Spark executor?

Answer: A

Explanation:
When deploying a Spark application on CDE service, which is managed on Kubernetes, you would typically use the CDE CLI to submit and configure your Spark jobs. The correct command would involve the cde spark submit command along with -conf flags to specify configuration options such as executor memory and cores. This approach is tailored to the CDE environment, leveraging its CLI for configuration rather than directly interfacing with Kubernetes (kubectl) or using the generic spark-submit command.

NEW QUESTION # 84
You want to use Spark to perform aggregations on data stored in Hive tables. How can you achieve this efficiently and seamlessly?

A. Leverage Spark SQL's built-in aggregation functions like SUM and COUNT
B. Write custom aggregation logic using Spark functions and loop through the entire DataFrame
C. Use HiveQL's aggregation capabilities and then convert the results back to a Spark DataFrame
D. Implement custom UDFs (User-Defined Functions) in Spark for complex aggregations

Answer: A

Explanation:
Option B provides the most efficient and recommended approach. Spark SQL offers various built-in functions for aggregations like SUM, COUNT, AVG, etc., enabling concise and efficient processing of data directly from Hive tables.

NEW QUESTION # 85
You're working with a complex DataFrame containing nested structures (e.g., arrays of structs). How can you access and manipulate data within these nested structures?

A. Implement custom recursive functions to navigate through the nested structure
B. Directly access elements using their position within the nested structure
C. Convert the nested data into a simpler format like a single-level DataFrame
D. Leverage Spark SQL's built-in functions like explode and struct

Answer: D

Explanation:
While direct access A might work for simple cases, it's not flexible for complex structures. Custom functions C can be complex and error-prone.
Spark SQL provides functions like explode to flatten arrays and struct to create complex structures, allowing you to efficiently work with and manipulate nested data.

NEW QUESTION # 86
......

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Ken King Ken King

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