Google Professional Machine Learning Engineer Question 454
Select 3Google Cloud PlatformYour team has deployed a machine learning model on Google Cloud that predicts loan defaults for a financial institution. After deployment, concerns are raised about potential risks associated with the AI solution. Which of the following strategies should you adopt to identify and mitigate risks effectively?
- A
Perform a bias analysis to identify if the model's predictions disproportionately affect certain demographic groups.
- B
Use Google Cloud’s Vertex Explainable AI to understand feature importance and detect potential model misbehavior.
- C
Disable logging of model predictions to avoid exposing sensitive customer data.
- D
Conduct regular model performance monitoring to ensure predictions remain accurate and relevant over time.
- E
Limit training data diversity to reduce the complexity of the model and make it easier to interpret.
Show answer and explanation
Correct answers: A, B, D
Explanation
Effectively identifying and mitigating risks in AI solutions requires a combination of fairness auditing (e.g., bias analysis), interpretability tools (e.g., Vertex Explainable AI), and ongoing monitoring of model performance. These practices are critical in ensuring that AI solutions remain robust, fair, and reliable during deployment. Disabling logging or reducing data diversity are harmful strategies that can hinder risk management.
- A. Correct.
Performing a bias analysis is crucial to identify any unintended discriminatory behavior in the model, especially in sensitive applications like loan approvals.
- B. Correct.
Using Vertex Explainable AI can help you detect which features are influencing predictions, allowing you to identify potential risks such as over-reliance on certain attributes.
- C. Incorrect.
Disabling logging of model predictions is not a recommended practice. Instead, sensitive data should be anonymized or secured during logging to maintain both utility and security.
- D. Correct.
Regular model performance monitoring ensures the model remains robust and detects any degradation in performance, which is essential for mitigating risks in production.
- E. Incorrect.
Limiting training data diversity is counterproductive, as it increases the likelihood of bias and reduces the model's ability to generalize effectively.