MLS-C01 exam dumps

MLS-C01 practice question 236 of 389

AWS Certified Machine Learning - Specialty. Expert level, Amazon Web Services. Free question with the correct answer and a full explanation.

MLS-C01 Question 236

Single answer

A data science team at a retail company has deployed a new machine learning model to recommend products to users. The team wants to compare the new model's performance against the existing model in a live environment to determine if it improves user engagement. Which approach should the team use to perform this evaluation?

  1. A

    Conduct offline evaluation using the historical dataset to compare the two models' performance metrics.

  2. B

    Perform online evaluation by setting up an A/B testing framework and splitting traffic between the two models.

  3. C

    Use cross-validation with the historical dataset to evaluate both models' accuracy and select the better one.

  4. D

    Deploy the new model in production for all users and compare its performance over time to the old model's historical performance.

Show answer and explanation

Correct answer: B

Explanation

A/B testing is a standard method for online evaluation where live traffic is split between multiple models to measure their performance in real-world conditions. This approach ensures the new model's effectiveness is assessed based on real-time user interactions. Offline evaluation methods like cross-validation or historical analysis are better suited for initial testing but do not account for live production dynamics.

  • A. Incorrect.

    Offline evaluation using a historical dataset is not suitable for comparing model performance in a live environment. It does not account for real-time user behavior or dynamic factors in the production environment.

  • B. Correct.

    A/B testing is the correct approach for online evaluation. It allows the team to split live traffic between the two models and directly compare their performance on real user interactions.

  • C. Incorrect.

    Cross-validation using a historical dataset is an offline evaluation method that is insufficient to determine performance in a live production environment.

  • D. Incorrect.

    Deploying the new model to all users without comparison may lead to negative business outcomes if the model underperforms. It also does not allow for a direct, controlled comparison with the old model.

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