CDP-3002 VALID TEST TUTORIAL | NEW CDP-3002 TEST TESTKING

CDP-3002 Valid Test Tutorial | New CDP-3002 Test Testking

CDP-3002 Valid Test Tutorial | New CDP-3002 Test Testking

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Tags: CDP-3002 Valid Test Tutorial, New CDP-3002 Test Testking, CDP-3002 Practice Exam, CDP-3002 Download Fee, CDP-3002 Exam

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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q289-Q294):

NEW QUESTION # 289
What are the potential challenges associated with schema inference in data processing pipelines?

  • A. The need for manual schema updates
  • B. Performance overhead due to schema discovery
  • C. Inaccuracies in inferred schemas leading to data processing errors
  • D. Handling complex nested structures and arrays
  • E. Increased storage costs for schema metadata

Answer: B,C,D

Explanation:
Schema inference can introduce performance overhead as the system needs to analyze the data to determine its structure. Inaccuracies in the inferred schema may occur, especially with complex data types or when the data does not follow a consistent format, leading to potential errors in data processing. Handling complex nested structures and arrays can also present challenges, as the inference mechanism must correctly identify these elements within the data.


NEW QUESTION # 290
A data analyst is performing a join operation in PySpark between a large DataFrame Clarge_df) and a much smaller DataFrame ('small_df). The process is very slow. What can the analyst do to optimize this join operation?

  • A. Increase the number of partitions in 'small_df'.
  • B. Use "key").
  • C. Utilize the 'broadcast' function in the join operation.
  • D. Apply before the join.

Answer: C

Explanation:
The 'broadcast' function is used in PySpark to optimize join operations where one DataFrame is significantly smaller than the other. It broadcasts the smaller DataFrame across all nodes so that it is available in memory, reducing the data shuffling over the network.


NEW QUESTION # 291
In a PySpark application, you're writing a function that reads a CSV file and shows the first few rows. Which of the following code snippets correctly accomplishes this task?

  • A. Option C
  • B. Option D
  • C. Option B
  • D. Option A

Answer: C

Explanation:
The correct way to read a CSV file in a PySpark application is using the 'SparkSession.read.csv' method or 'SparkSession.read.format("csv").load'. After loading the data into a DataFrame, 'df.show(5)' is used to display the first five rows. Options A and C do not use the correct method to display the data, and option D uses Pandas, not PySpark.


NEW QUESTION # 292
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 'MyPySparkApp-0.1-py3-none-any.whl' only and submit using 'spark-submit --py-files MyPySparkApp-0.1-py3-none-any.whl'.
  • B. Upload 'app.py' only and submit using 'spark-submit app.py'.
  • C. Upload 'app.py' and manually install 'numpy' on all nodes before submitting using 'spark-submit app.py'.
  • D. 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'.

Answer: D

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 # 293
You need to filter a Spark DataFrame based on multiple conditions. How can you achieve this efficiently and concisely?

  • A. Use multiple filter() calls with individual conditions
  • B. Leverage chained filter() calls with logical operators like AND and OR
  • C. Implement custom filtering logic using loops and conditional statements
  • D. Use Spark SQL's WHERE clause with a complex expression

Answer: B

Explanation:
While using multiple independent filter() calls A works, it can be less readable. Chaining filter() calls B with logical operators like & (AND. and I (OR) offers a concise and efficient way to filter based on multiple conditions. Option C is inefficient and error-prone, while D might be suitable for complex queries but is less versatile for simpler filtering operations.


NEW QUESTION # 294
......

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