Question 3
Domain 6In a production ML workflow, what is the primary purpose of model interpretability tools such as SHAP, LIME, or feature attribution methods?
Correct answer: C
Explanation
Model interpretability tools like SHAP, LIME, and feature attribution methods explain how inputs influence a prediction, making model behavior understandable to stakeholders. In a production ML workflow, this supports aligning predictions with business objectives by showing which features drive outcomes and why.
Why each option is right or wrong
A. Creating neural network architectures
B. Performing data preprocessing.
C. Ensuring that stakeholders understand how model predictions relate to business objectives.
SHAP, LIME, and feature attribution methods are used to explain which input features most influenced a given prediction and in what direction, so the model’s behavior can be inspected by non-technical stakeholders. In a production ML setting, that explanation layer is what lets teams verify that the model’s outputs are consistent with the intended business objective, rather than treating the model as a black box.
D. Collecting raw training data