Google Professional Machine Learning Engineer exam dumps

Google Professional Machine Learning Engineer practice question 477 of 522

Professional Machine Learning Engineer. Professional level, Google Cloud. Free question with the correct answer and a full explanation.

Google Professional Machine Learning Engineer Question 477

Select 3Google Cloud Platform

A retail company is deploying a machine learning model to recommend products to its customers. During the evaluation phase, you discover that the model's recommendations are significantly biased against certain demographic groups. What steps should you take to assess the AI solution's readiness and address the issue?

  1. A

    Perform a fairness evaluation by testing the model's performance across different demographic groups.

  2. B

    Monitor the training data for imbalances or underrepresentation of specific groups and adjust the data accordingly.

  3. C

    Deploy the model immediately and address fairness issues after collecting real-world user feedback.

  4. D

    Use Google's What-If Tool to analyze feature importance and detect potential sources of bias in the model.

  5. E

    Ignore the bias issue, as it may not significantly affect the company's revenue.

Show answer and explanation

Correct answers: A, B, D

Explanation

Ensuring fairness and addressing bias are critical steps in assessing the readiness of an AI solution, especially for models that impact diverse user groups. Steps such as fairness evaluation, data balance checks, and utilizing bias analysis tools like Google's What-If Tool help identify and mitigate bias, ensuring the solution is both ethical and effective before deployment.

  • A. Correct.

    Performing a fairness evaluation is a critical step to identify discrepancies in the model's performance across demographic groups and assess its readiness for deployment.

  • B. Correct.

    Checking for imbalances in the training data and making necessary adjustments ensures that the model is trained on a representative dataset, reducing potential bias.

  • C. Incorrect.

    Deploying the model without addressing fairness issues may lead to ethical concerns and reputational damage, making this option inappropriate.

  • D. Correct.

    Using tools like Google's What-If Tool helps analyze the model's behavior, identify sources of bias, and take corrective actions, making it a crucial step in assessing AI readiness.

  • E. Incorrect.

    Ignoring the bias issue is not acceptable, as fairness and ethical AI practices are essential, regardless of their immediate impact on revenue.

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