ATTAIN 100% SUCCESS WITH CLOUDERA CDP-3002 EXAM QUESTIONS ON YOUR FIRST ATTEMPT

Attain 100% Success with Cloudera CDP-3002 Exam Questions on Your First Attempt

Attain 100% Success with Cloudera CDP-3002 Exam Questions on Your First Attempt

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Tags: Instant CDP-3002 Download, CDP-3002 Latest Exam Vce, New CDP-3002 Test Syllabus, CDP-3002 Exam Fees, Training CDP-3002 Pdf

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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q271-Q276):

NEW QUESTION # 271
In the context of Spark SQL, what does the Catalyst optimizer use to optimize queries?

  • A. A cost-based optimization model that considers the size of intermediate data
  • B. A rule-based optimization model that applies predefined rules to simplify queries
  • C. Machine learning algorithms to predict the fastest query execution plan

Answer: A,B

Explanation:
The Catalyst optimizer in Spark SQL uses both a cost-based optimization model and a rule-based optimization model. The cost-based model considers factors like the size of intermediate data to choose the most efficient query execution plan, while the rule-based model applies a series of predefined rules to simplify and optimize queries. This combination allows Spark SQL to efficiently execute queries by optimizing both the logical and physical plans. Machine learning algorithms (Option C. are not used by the Catalyst optimizer for this purpose.


NEW QUESTION # 272
You need to enable secure access to Iceberg tables in CDP, controlling permissions at the table, column, and row level. Which of the following approaches would you investigate?

  • A. Leverage Apache Ranger's integration with Iceberg.
  • B. Configure role-based access control (RBA directly within Iceberg table properties.
  • C. Integrate with CDP's Kerberos-based authentication system.
  • D. Implement custom Spark IJDFs to enforce authorization logic.

Answer: A

Explanation:
Apache Ranger provides fine-grained authorization for Hive and other data platforms and is increasingly integrated with Iceberg for centralized policy management. The other options are less suitable for comprehensive access control.


NEW QUESTION # 273
You have a PySpark application packaged as 'MyPySparkApp-0. I-py3-none-any.whl'. In your 'app.py', you utilize a function from an external library, 'numpy', listed in your 'requirements.txt'. How should you deploy this application to ensure 'numpy' is available at runtime?

  • A. Upload 'app.py' only and submit using 'spark-submit app.py'.
  • B. Upload both 'app.py' and 'MyPySparkApp-0.1-py3-none-any.whl' and submit using 'spark-submit --py-files MyPySparkApp-0.1-py3-none- any.whl app.py'.
  • C. Upload 'app.py' and manually install 'numpy' on all nodes before submitting using 'spark-submit app.py'.
  • D. Upload 'MyPySparkApp-0.1-py3-none-any.whl' only and submit using 'spark-submit --py-files MyPySparkApp-0.1-py3-none-any.whl'.

Answer: B

Explanation:
The application 'app.py' depends on the 'numpy' library, which is packaged in the wheel file 'MyPySparkApp-0.1-py3-none- any.whl'. Therefore, both the application file and the wheel file need to be uploaded. The application is then submitted with the '-- py-files' option to include the wheel file, ensuring 'numpy' is available at runtime.


NEW QUESTION # 274
When writing a DataFrame to a CSV file, what potential issues should you consider and how can you address them?

  • A. All of the above
  • B. Ensure proper handling of special characters and delimiters to avoid data corruption
  • C. No specific issues need to be considered, as CSV is a simple format
  • D. Choose an appropriate compression format like Gzip to reduce file size

Answer: A

Explanation:
CSV files can present challenges. Special characters and delimiters B need proper handling to avoid misinterpretations. Compression C like Gzip can significantly reduce file size without data loss. Considering all these aspects ensures efficient and reliable storage of DataFrame data in CSV format.


NEW QUESTION # 275
Your Airflow DAG includes tasks that can potentially fail due to various reasons. How can you handle such failures and ensure the overall workflow continues as intended?

  • A. All of the above
  • B. Configure the DAG to automatically retry failed tasks a specific number of times.
  • C. Utilize Airflow XCom to share information about failed tasks with downstream tasks for alternative processing.
  • D. Implement custom logic within each task to handle potential errors and retry failed tasks manually.

Answer: A,B

Explanation:
While option A might work for simple cases, it's not efficient for event-driven workflows. Option B highlights the use of Airflow sensors:Airflow Sensors: Allow the DAG to wait for specific conditions (e.g., a file to appear, a specific key in S3) before triggering, ensuring timely execution based on external events or data availability.


NEW QUESTION # 276
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