Databricks Machine Learning Professional exam dumps

Databricks Machine Learning Professional practice question 162 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 162

Select 3

A data science team is tasked with scoring a large batch of incoming data for fraud detection using a pre-trained machine learning model in Databricks. The team decides to use the score_batch operation instead of scoring the data in real-time. What are the practical benefits of using the score_batch operation in this scenario?

  1. A

    It allows scoring large volumes of data more efficiently by processing data in batches.

  2. B

    It reduces the need to maintain an always-on endpoint for real-time inference.

  3. C

    It provides better prediction accuracy compared to real-time scoring methods.

  4. D

    It enables integration with scheduled workflows, such as ETL pipelines, for periodic batch processing.

  5. E

    It automatically tunes hyperparameters for the model before scoring the data.

Show answer and explanation

Correct answers: A, B, D

Explanation

The score_batch operation is ideal for scenarios where large datasets need to be scored efficiently without the need for real-time inference. It reduces operational overhead by not requiring an always-on endpoint and can be seamlessly integrated with scheduled workflows, such as batch ETL pipelines. However, it does not modify the model or improve accuracy, as it relies on a pre-trained model for inference.

  • A. Correct.

    Correct: The score_batch operation is optimized for batch processing, making it more efficient for handling large datasets as opposed to scoring individual records in real-time.

  • B. Correct.

    Correct: Batch scoring does not require an always-on endpoint, which is necessary for real-time scoring. This reduces operational costs and complexity.

  • C. Incorrect.

    Incorrect: The score_batch operation does not improve prediction accuracy; it uses the same pre-trained model as real-time scoring. Accuracy depends on the model, not the scoring method.

  • D. Correct.

    Correct: Batch scoring can be integrated with scheduled workflows (e.g., ETL jobs) to process data periodically, aligning well with batch processing strategies.

  • E. Incorrect.

    Incorrect: The score_batch operation does not involve hyperparameter tuning. It simply applies an existing trained model to the input data.

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