Google Professional Machine Learning Engineer exam dumps

Google Professional Machine Learning Engineer practice question 493 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 493

Select 2Google Cloud Platform

You are designing a machine learning model for loan approval predictions, and the stakeholders require the system to provide clear justifications for its decisions. How can you leverage Google Cloud's Explainable AI tools to meet this requirement?

  1. A

    Use Vertex AI Explainable AI to generate feature attributions for each prediction.

  2. B

    Enable Explainable AI while training the model in Vertex AI to ensure the model inherently provides explanations.

  3. C

    Integrate the What-If Tool to analyze model predictions and test potential scenarios.

  4. D

    Use AI Explanations to limit the model's ability to leverage sensitive features during training.

  5. E

    Set up Explainable AI with pre-trained models in Vertex AI to provide explanations without retraining.

Show answer and explanation

Correct answers: A, C

Explanation

To deliver clear justifications for model decisions, you can use Vertex AI Explainable AI to generate feature attributions and the What-If Tool to analyze predictions and simulate different scenarios. Together, these tools provide robust insights into model behavior and assist in meeting stakeholder requirements.

  • A. Correct.

    Vertex AI Explainable AI provides feature attributions that demonstrate the contribution of each feature to a model's prediction. This is a key tool for meeting the requirement for justifications.

  • B. Incorrect.

    Explainable AI is not a training-time feature but is applied to trained models. Enabling it during training is not a valid approach.

  • C. Correct.

    The What-If Tool can be used to analyze and visualize model behavior, helping stakeholders understand model predictions and test scenarios, which aligns with the requirement.

  • D. Incorrect.

    AI Explanations do not directly limit the usage of sensitive features; rather, they focus on providing interpretability for model predictions.

  • E. Incorrect.

    Explainable AI can be set up with trained models, but pre-trained models may not always align with the specific use case, and retraining might be necessary for accurate explanations.

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