Q57 — AWS AIF-C01 Ch.2

Question 57 of 100 | ← Chapter 2

A company performs quarterly forecasts to optimize operations and meet anticipated demand. It uses machine learning models for these forecasts. A data scientist must author a report on the trained machine learning model to provide transparency and explainability to stakeholders.

• A. Model training code
• B. Partial Dependence Plots (PDPs) ✓
• C. Training sample data and model convergence table

Correct Answer: B. Partial Dependence Plots (PDPs)

Explanation

To satisfy transparency and explainability requirements, the data scientist should include Partial Dependence Plots (PDPs) in the report. PDPs are visualization tools that illustrate the relationship between a specific feature (or independent variable) and the predicted outcome, marginalizing over the effects of other features. This helps stakeholders understand how predictions are influenced by individual features, offering intuitive insight into the model’s decision logic and enhancing both transparency and explainability. Other options—such as training code, sample data, or convergence tables—are valuable for development and validation but do not directly fulfill transparency and explainability goals.