Question 511 of 1,000

Ethical AI and Data Privacy →mediumMultiple ChoiceObjective-mapped

How to Detect Bias in AI Training Data

This AI Associate practice question tests your understanding of ethical ai and data privacy. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.

A data scientist is building a model to recommend products. They notice the model rarely recommends certain categories to users from a specific demographic. What should the scientist do first to address potential bias?

Clue words in this question

Noticing these words before you look at the options changes how you read each choice.

Clue: "first"
Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.

A
Increase the model's complexity to capture more patterns
Why wrong: Complexity does not fix bias; it may exacerbate it.
B
Add more features about the demographic to improve accuracy
Why wrong: Adding more demographic features could increase bias.
C
Audit the training data for representation and bias
Data audit identifies if underrepresentation or biased labels exist.
D
Remove the demographic feature from the model
Why wrong: Removing the feature may not eliminate bias if other features correlate with the demographic.

Full breakdown with real-world context →

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 representation and bias

Option C is correct because the first step in addressing potential bias is to audit the training data for representation and bias. This involves examining whether certain categories or demographics are underrepresented or overrepresented in the dataset, which can lead to skewed model recommendations. Without understanding the data composition, other interventions may fail to address the root cause of bias.

Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Increase the model's complexity to capture more patterns
Why it's wrong here
Complexity does not fix bias; it may exacerbate it.
✗
Add more features about the demographic to improve accuracy
Why it's wrong here
Adding more demographic features could increase bias.
✓
Audit the training data for representation and bias
Why this is correct
Data audit identifies if underrepresentation or biased labels exist.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Remove the demographic feature from the model
Why it's wrong here
Removing the feature may not eliminate bias if other features correlate with the demographic.

Common exam traps

Common exam trap: answer the scenario, not the keyword

Salesforce often tests the misconception that bias is solely caused by the presence of sensitive features, leading candidates to choose removing the feature (Option D) instead of recognizing that bias originates from the training data and can persist through proxy features.

Detailed technical explanation

How to think about this question

Bias in machine learning models often stems from imbalanced training data where certain groups are underrepresented. Auditing involves statistical analysis of feature distributions, label distributions, and intersectional group representation. For example, in a product recommendation system, if a demographic group has few purchase records for a category, the model may rarely recommend it, and this can be detected through stratified cross-validation or fairness metrics like demographic parity.

KKey Concepts to Remember

Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.

TExam Day Tips

Watch for words such as best, first, most likely and least administrative effort.
Review why wrong options are wrong, not only why the correct option is correct.

Key takeaway

Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Real-world example

How this comes up in practice

A practitioner preparing for the AI Associate exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.

What to study next

Got this wrong? Here's your next step.

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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FAQ

Questions learners often ask

What does this AI Associate question test?

Ethical AI and Data Privacy — This question tests Ethical AI and Data Privacy — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: Audit the training data for representation and bias — Option C is correct because the first step in addressing potential bias is to audit the training data for representation and bias. This involves examining whether certain categories or demographics are underrepresented or overrepresented in the dataset, which can lead to skewed model recommendations. Without understanding the data composition, other interventions may fail to address the root cause of bias.

What should I do if I get this AI Associate question wrong?

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

Are there clue words in this question I should notice?

Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

About these practice questions

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Same concept, more angles

1 more ways this is tested on AI Associate

These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.

Variation 1. A data scientist is building a custom AI model using Salesforce Data Cloud to predict customer churn. They want to ensure that the model does not inadvertently use gender as a feature to avoid biased predictions. Which step is MOST appropriate?

hard

✓ A.Exclude gender from the feature set used for model training
B.Use gender as a feature but ignore the model predictions for certain groups
C.Allow gender in training but use a post-processing technique to adjust scores
D.Include gender as a feature and then apply a fairness constraint during training

Why A: The best practice is to exclude protected attributes from the model features. Data minimisation supports this. Auditing for bias is also important but does not prevent the model from using the attribute in the first place.

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Question Discussion

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