HOT CDP-3002 SPOT QUESTIONS - CDP-3002 LATEST TEST MATERIALS

Hot CDP-3002 Spot Questions - CDP-3002 Latest Test Materials

Hot CDP-3002 Spot Questions - CDP-3002 Latest Test Materials

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Tags: Hot CDP-3002 Spot Questions, CDP-3002 Latest Test Materials, Flexible CDP-3002 Learning Mode, CDP-3002 Exam Price, Exam Vce CDP-3002 Free

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>> Hot CDP-3002 Spot Questions <<

CDP-3002 Latest Test Materials, Flexible CDP-3002 Learning Mode

The ExamDumpsVCE CDP Data Engineer - Certification Exam (CDP-3002) PDF format of questions is user-friendly, portable, and printable that's easy to use on smartphones, laptops, and tablets. This way, you can prepare for the CDP-3002 test anywhere without time restrictions. For those who prefer a traditional reading experience, ExamDumpsVCE CDP Data Engineer - Certification Exam (CDP-3002) PDF questions also provides the option to print the CDP-3002 questions, and read it in a convenient paper format. This flexibility empowers CDP-3002 candidates to study anywhere and anytime, adapting to their individual preferences and schedules.

Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q311-Q316):

NEW QUESTION # 311
What mechanism does Airflow provide to retry failed tasks?

  • A. The retry_delay and retries parameters in task definitions
  • B. Airflow Scheduler's automatic rerun feature
  • C. Manual intervention and rerun via the Airflow Webserver
  • D. The on failure callback function in DAG definitions

Answer: A

Explanation:
Airflow allows task retries by specifying retries (the number of retry attempts) and retry_delay (the time to wait between retries) parameters directly in the task definitions. This mechanism enables automatic retry of tasks that fail, helping to handle transient issues or dependencies that may not be ready, without needing manual intervention or relying on callbacks for handling failures.


NEW QUESTION # 312
You encounter an error during the execution of your Airflow DAG. How can you identify the root cause of the issue and debug it effectively?

  • A. All of the above
  • B. Utilize Airflow logs (scheduler, worker) to analyze detailed error messages and stack traces.
  • C. Rely on the error message displayed on the Airflow UI, even if it's generic.
  • D. Inspect the code of failed tasks directly within the Airflow web Ul for potential issues.

Answer: A

Explanation:
While logging can be valuable (option C. , extensive logging can clutter the code. Option B emphasizes best practices for improving DAG readability and maintainability:Sub-DAGs: Break down the DAG into smaller, reusable units (sub-DAGs) with clear naming and well-defined functionalities. Naming Conventions: Use descriptive and consistent names for tasks, variables, and DAGs, improving code comprehension.


NEW QUESTION # 313
You're working with a CSV file containing missing dat
a. How can you efficiently handle missing values in a Spark DataFrame created from this file?

  • A. Implement custom logic to identify and handle missing values individually
  • B. Use the fillna() function with an appropriate replacement value
  • C. Convert the DataFrame to a different format that doesn't support missing values
  • D. Ignore the rows with missing values during DataFrame creation

Answer: B

Explanation:
Ignoring rows A can lead to data loss. Custom logic C is inefficient and error-prone. Converting the format D might not be feasible or desirable. The fillna() function B allows you to specify a replacement value (e.g., 0, mean) for missing values, ensuring consistent data handling.


NEW QUESTION # 314
How does Spark handle data shuffling during distributed processing?

  • A. By transferring only required data between executors
  • B. Spark doesn't perform data shuffling
  • C. By broadcasting all data to each executor
  • D. By storing all data on a single node

Answer: A

Explanation:
Shuffling involves moving data between executors when operations require data from different partitions. Spark optimizes this process by transferring only the necessary data, minimizing network traffic.


NEW QUESTION # 315
Given a DataFrame containing product information with columns "product_id", "name", and "price", how can you filter and sort the DataFrame to only include products with a price greater than $50 and sort them by price in descending order?

  • A. Use multiple filter and sort calls sequentially
  • B. Implement a custom function to filter and sort the data
  • C. Leverage chained conditions and sorting within the filter method
  • D. Use spark SQL's WHERE clause and ORDER BY statement

Answer: C

Explanation:
Chaining conditions within the filter method allows for concise and efficient filtering. Option B demonstrates this approach:filtered_sorted_df = ] 50).sort("price", ascending=FalsE.
This code first filters the DataFrame to only include rows where the "price" is greater than 50 and then sorts the resulting DataFrame by the "price" column in descending order (using ascending=FalsE.


NEW QUESTION # 316
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