Databricks Machine Learning Professional Question 163
Select 3A data science team has trained a machine learning model on Databricks and plans to use it for scoring large batches of incoming data. They are considering using the score_batch operation. Which of the following are practical benefits of using the score_batch operation in this scenario?
- A
It allows for efficient scoring of large datasets without requiring a real-time endpoint.
- B
It ensures that the model automatically retrains itself during the scoring process.
- C
It is optimized to handle distributed data processing across a Databricks cluster.
- D
It reduces the cost and complexity of deploying a separate REST API for scoring.
- E
It enforces stricter security policies for model scoring compared to real-time scoring.
Show answer and explanation
Correct answers: A, C, D
Explanation
The score_batch operation is designed for batch scoring scenarios, where efficiency, scalability, and simplicity are key concerns. It allows large datasets to be scored using the distributed capabilities of Databricks, while avoiding the overhead of deploying real-time endpoints. This makes it a practical choice for teams working with pre-scheduled or large-scale data scoring workloads.
- A. Correct.
This is correct. The
score_batchoperation processes large datasets efficiently without the need for a real-time scoring endpoint, making it ideal for batch processing scenarios. - B. Incorrect.
This is incorrect. The
score_batchoperation is used for scoring data and does not involve retraining the model as part of its functionality. - C. Correct.
This is correct. The
score_batchoperation utilizes the distributed computing capabilities of Databricks, making it well-suited for large-scale data processing. - D. Correct.
This is correct. By using the
score_batchoperation, the team avoids the need to deploy and maintain a separate infrastructure (such as a REST API) for scoring, simplifying the workflow and potentially reducing costs. - E. Incorrect.
This is incorrect. The
score_batchoperation does not specifically enforce stricter security policies compared to real-time scoring; its primary focus is on batch processing efficiency.