Google Cloud Professional Machine Learning Engineer — Question 303

You are developing an ML model on Vertex AI that needs to meet specific interpretability requirements for regulatory compliance. You want to use a combination of model architectures and modeling techniques to maximize accuracy and interpretability. How should you create the model?

Answer options

• A. Use a convolutional neural network (CNN)-based deep learning model architecture, and use local interpretable model-agnostic explanations (LIME) for interpretability.
• B. Use a recurrent neural network (RNN)-based deep learning model architecture, and use integrated gradients for interpretability.
• C. Use a boosted decision tree-based model architecture, and use SHAP values for interpretability.
• D. Use a long short-term memory (LSTM)-based model architecture, and use local interpretable model-agnostic explanations (LIME) for interpretability.

Correct answer: C

Explanation

The correct answer is C because boosted decision trees are known for their high accuracy and SHAP values provide a strong method for interpreting model predictions, making them suitable for regulatory compliance. Options A and D involve models that may not achieve the same level of accuracy or interpretability compared to boosted decision trees with SHAP, while option B, using RNNs, typically excels in sequential data but may not meet the interpretability needs as effectively.