AI Associate AI Fundamentals Practice Question
A financial services company is deploying Einstein Prediction Builder to predict customer churn. The data includes both numerical and categorical fields. Which step is essential to ensure the model is not biased against protected attributes like race or gender?
⚠ Common exam trap
Salesforce often tests the misconception that including protected attributes allows the model to 'adjust' for bias, when in reality it introduces direct bias, and that built-in fairness constraints or advanced algorithms can automatically fix bias without explicit data preparation.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Exclude any protected attributes from the training data and ensure the model does not use correlated proxies.
Excluding protected attributes like race or gender from the training data and ensuring the model does not use correlated proxies is essential to prevent bias in Einstein Prediction Builder. This approach directly removes the risk of the model learning discriminatory patterns based on these attributes, as the platform relies on the data provided and does not automatically enforce fairness constraints. Including such attributes or relying on built-in fairness would not guarantee unbiased predictions because the model could still infer protected characteristics from correlated features.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Include race and gender as predictors to allow the model to adjust for them.
Why it's wrong here
Including them can introduce or amplify bias.
- ✗
Rely on the model's built-in fairness constraints.
Why it's wrong here
Einstein Prediction Builder does not include fairness constraints.
- ✗
Use a deep learning algorithm to automatically handle bias correction.
Why it's wrong here
Deep learning does not automatically correct bias; careful data preparation is needed.
- ✓
Exclude any protected attributes from the training data and ensure the model does not use correlated proxies.
Why this is correct
This is the standard approach to mitigate bias.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI Associate practice question is part of Courseiva's free Salesforce certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI Associate exam.