Databricks Machine Learning Professional exam dumps

Databricks Machine Learning Professional practice question 139 of 280

Databricks Certified Machine Learning Professional. Professional level, Databricks. Free question with the correct answer and a full explanation.

Databricks Machine Learning Professional Question 139

Select 3

A data science team is building a machine learning solution for real-time fraud detection on a financial transaction dataset. To handle high query volumes with low latency, they decide to serve precomputed batch predictions using a feature store. What are the benefits of querying precomputed batch predictions in this scenario?

  1. A

    Reduced serving latency since predictions are already precomputed

  2. B

    Increased prediction accuracy due to precomputed batch processing

  3. C

    Lower computational cost during live serving

  4. D

    Simplified infrastructure for handling large-scale requests

  5. E

    Ability to dynamically retrain the model during live serving

Show answer and explanation

Correct answers: A, C, D

Explanation

Querying precomputed batch predictions is beneficial in scenarios requiring low latency and high scalability, such as real-time fraud detection. By precomputing predictions during a batch process, the system reduces serving latency, lowers computational cost at query time, and simplifies the infrastructure needed to handle large-scale requests. However, this approach does not improve model accuracy or allow for dynamic retraining during live serving.

  • A. Correct.

    Precomputed batch predictions allow the system to retrieve results without running the model in real-time, significantly reducing serving latency.

  • B. Incorrect.

    Prediction accuracy is not inherently improved by precomputing predictions; it depends on the quality of the model and data, not the serving method.

  • C. Correct.

    Since predictions are precomputed, the system does not need to utilize compute resources for real-time model inference, reducing the computational cost.

  • D. Correct.

    Precomputing predictions simplifies the serving infrastructure by offloading model inference to the batch processing phase, making it easier to scale for large request volumes.

  • E. Incorrect.

    Precomputing predictions does not enable dynamic retraining. Model retraining is a separate process and unrelated to how predictions are served.

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