Databricks Machine Learning Professional Question 161
Select 3You are working on a machine learning pipeline in Databricks, where you need to score a batch of predictions for a large dataset in a production environment. Why would you use the score_batch operation instead of scoring each record individually?
- A
It improves performance by processing multiple records in parallel.
- B
It ensures that the predictions are more accurate compared to scoring records one-by-one.
- C
It reduces overhead by avoiding repeated model loading for each record.
- D
It provides better logging and monitoring for individual predictions.
- E
It is specifically optimized for batch processing in distributed environments.
Show answer and explanation
Correct answers: A, C, E
Explanation
The score_batch operation is beneficial in scenarios where large datasets need to be processed efficiently. It leverages parallelism, avoids repeated overhead like model loading, and is optimized for distributed environments, making it an ideal choice for batch processing in production pipelines.
- A. Correct.
This is correct because
score_batchis designed to handle multiple records simultaneously, leveraging parallelism for better performance. - B. Incorrect.
This is incorrect because the accuracy of predictions does not depend on whether records are scored individually or in a batch.
- C. Correct.
This is correct because
score_batchavoids the repetitive task of loading the model for each record, reducing computational overhead. - D. Incorrect.
This is incorrect because
score_batchdoes not inherently provide better logging or monitoring for individual predictions. - E. Correct.
This is correct because
score_batchis optimized for distributed environments like Databricks, making it ideal for handling large datasets efficiently.