CDP-3002 PDF FORMAT | EXAMCOLLECTION CDP-3002 FREE DUMPS

CDP-3002 Pdf Format | Examcollection CDP-3002 Free Dumps

CDP-3002 Pdf Format | Examcollection CDP-3002 Free Dumps

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Tags: CDP-3002 Pdf Format, Examcollection CDP-3002 Free Dumps, Latest CDP-3002 Study Guide, Valid Dumps CDP-3002 Ppt, Reliable CDP-3002 Braindumps Ebook

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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q295-Q300):

NEW QUESTION # 295
Which Spark component is responsible for managing the execution of tasks on worker nodes?

  • A. Spark Executor
  • B. Spark Core
  • C. Spark Driver
  • D. spark SQL

Answer: A

Explanation:
Each worker node runs an executor responsible for receiving tasks from the driver, fetching data from storage, and performing computations on assigned partitions.


NEW QUESTION # 296
You encounter an error message stating "Failed to find persisted data for RDD" in your Spark application. What are the potential causes and how can you troubleshoot them?

  • A. Spark encountered a network issue and couldn't access the persisted data
  • B. The RDD was never persisted, or the storage level was set incorrectly
  • C. The data might have been corrupted or deleted from the storage location
  • D. All of the above

Answer: D

Explanation:
The error message indicates missing persisted data. Potential causes include A not persisting the RDD or using an inadequate storage level, B network issues preventing data access, or C data corruption/deletion. Examining the application logic, storage configuration, and Spark logs can help identify the specific cause and take corrective actions.


NEW QUESTION # 297
Your Airflow DAG includes data quality checks that involve comparing data against predefined thresholds or reference datasets. How can you handle potential failures during these checks and ensure the pipeline doesn't proceed with unreliable data?

  • A. Configure the DAG to automatically retry failed tasks associated with the data quality checks.
  • B. Implement custom logic within the PythonOperator to raise exceptions upon failed checks and trigger downstream tasks to handle the error.
  • C. Use the BranchPythonOperator to check the success of the data quality checks and conditionally branch the DAG execution based on the outcome.
  • D. All of the above

Answer: C,D

Explanation:
While option B might be applicable for specific data sources, option D is the most comprehensive approach. You can combine:'PythonOperator': Provides flexibility to implement custom logic for various data validation tasks using Python libraries like Pandas or Spark (option C.. Custom Python Scripts: Can encapsulate the validation logic for reusability and modularity. Chaining Operators: Allow you to define dependencies and ensure checks are executed in the desired order.


NEW QUESTION # 298
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. Leverage Spark SQL's built-in functions like explode and struct
  • B. Convert the nested data into a simpler format like a single-level DataFrame
  • C. Implement custom recursive functions to navigate through the nested structure
  • D. Directly access elements using their position within the nested structure

Answer: A

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 # 299
A PySpark application is facing performance issues due to uneven distribution of data across the nodes. Which approach would best help in resolving this issue?

  • A. Apply 'df.coalesce(Y to reduce the number of partitions.
  • B. Use 'df.collect()'.
  • C. Convert the DataFrame to an RDD and then back to a DataFrame.
  • D. Employ to redistribute data based on a key.

Answer: D

Explanation:
Using in PySpark helps to redistribute the data more evenly based on the specified key column. This can alleviate issues caused by data skewness and improve the performance of distributed operations.


NEW QUESTION # 300
......

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