Exam Professional Machine Learning Engineer topic 1 question 316 discussion

Actual exam question from Google's Professional Machine Learning Engineer

Question #: 316
Topic #: 1

[All Professional Machine Learning Engineer Questions]

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?

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.

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Suggested Answer: C 🗳️

by hit_cloudie at May 21, 2025, 8:50 a.m.

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qaz09

1 month ago

Selected Answer: C

Boosted trees model is the only highly interpratable model here. The rest is more "black box" solutions

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hit_cloudie

2 months, 3 weeks ago

Selected Answer: C

Boosted decision trees (e.g., XGBoost, LightGBM) offer high accuracy on structured data and work well in production. SHAP (SHapley Additive exPlanations) provides strong theoretical interpretability and is accepted in regulatory environments.

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