AI Associate Data for AI Practice Question
A company is deploying an AI model that recommends next best actions for sales reps. They notice that the model's recommendations are biased towards high-revenue opportunities. Which data-related action can help reduce this bias?
⚠ Common exam trap
Salesforce often tests the misconception that model architecture changes (like larger networks) can fix data bias, when in fact the root cause is often data imbalance that must be addressed at the data level.
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
✓
Oversample the underrepresented segments in the training data
Oversampling underrepresented segments in the training data directly addresses the class imbalance that causes the model to favor high-revenue opportunities. By increasing the frequency of low-revenue examples, the model learns to treat all segments more equally, reducing bias in its recommendations. This is a standard data-level technique for mitigating bias in AI models.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a larger neural network model
Why it's wrong here
Model size does not directly address data bias.
- ✗
Encrypt the data before training
Why it's wrong here
Encryption does not affect bias.
- ✓
Oversample the underrepresented segments in the training data
Why this is correct
Oversampling helps balance the representation.
- ✗
Remove all low-revenue opportunities from the training data
Why it's wrong here
Removing data can increase 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.