AI Associate AI Fundamentals Practice Question
A company is developing a sentiment analysis model for customer reviews. The team wants to ensure the model is fair and does not exhibit bias. Which TWO actions are MOST effective? (Choose two.)
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
✓
Audit the training data for imbalanced representation across demographic groups
Auditing training data for bias and ensuring diverse representation helps mitigate bias. Using a complex model doesn't address bias; removing all negative examples would bias the model. Using historical data as-is may perpetuate existing biases.
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 more complex model to capture nuanced patterns
Why it's wrong here
Complexity does not guarantee fairness and may amplify biases.
- ✓
Audit the training data for imbalanced representation across demographic groups
Why this is correct
Identifying and correcting imbalances helps reduce bias.
- ✗
Use only historical data as-is without modification
Why it's wrong here
Historical data may contain biases that need correction.
- ✓
Ensure the training data includes reviews from a diverse range of customers
Why this is correct
Diverse data helps the model generalize fairly.
- ✗
Remove all negative reviews from the training data to avoid negative bias
Why it's wrong here
Removing entire classes distorts the model's understanding.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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