- A
The model's algorithm is inherently biased against certain regions
Why wrong: Algorithms are not inherently biased; bias comes from data or feature selection.
- B
The training data contains biased outcomes from past human decisions
Historical bias in the data leads the model to replicate those biases.
- C
The model overfits to the training data
Why wrong: Overfitting would cause poor generalization but not necessarily bias against specific postal codes.
- D
The model was not trained long enough
Why wrong: Training duration does not cause systematic bias against specific groups.
Question 680 of 1,000
Bias in Lead Scoring: Root Cause
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 lead scoring model trained on historical sales data is found to assign lower scores to leads from certain postal codes. What is the MOST likely cause?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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
✓
The training data contains biased outcomes from past human decisions
The model assigns lower scores to leads from certain postal codes because the training data reflects historical human biases, such as sales representatives prioritizing leads from affluent areas. Machine learning models learn patterns from the data they are trained on; if past sales decisions were biased against certain regions, the model will replicate those biases. This is a classic case of bias in training data leading to biased model outcomes, not an inherent flaw in the algorithm itself.
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.
- ✗
The model's algorithm is inherently biased against certain regions
Why it's wrong here
Algorithms are not inherently biased; bias comes from data or feature selection.
- ✓
The training data contains biased outcomes from past human decisions
Why this is correct
Historical bias in the data leads the model to replicate those biases.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
The model overfits to the training data
Why it's wrong here
Overfitting would cause poor generalization but not necessarily bias against specific postal codes.
- ✗
The model was not trained long enough
Why it's wrong here
Training duration does not cause systematic bias against specific groups.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Salesforce often tests the misconception that algorithmic bias is caused by the model's internal logic or training duration, whereas the root cause is almost always biased training data reflecting past human decisions.
Detailed technical explanation
How to think about this question
Bias in lead scoring models often stems from historical data where sales teams disproportionately contacted leads from certain areas, creating a feedback loop. The model learns to associate those postal codes with lower conversion likelihood, even if the true potential is equal. In practice, this requires techniques like reweighing training samples, using fairness constraints, or applying adversarial debiasing to mitigate such biases.
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.
- →
Ethical AI and Data Privacy — study guide chapter
Learn the concepts, then practise the questions
- →
Ethical AI and Data Privacy practice questions
Targeted practice on this topic area only
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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: The training data contains biased outcomes from past human decisions — The model assigns lower scores to leads from certain postal codes because the training data reflects historical human biases, such as sales representatives prioritizing leads from affluent areas. Machine learning models learn patterns from the data they are trained on; if past sales decisions were biased against certain regions, the model will replicate those biases. This is a classic case of bias in training data leading to biased model outcomes, not an inherent flaw in the algorithm itself.
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: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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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Last reviewed: Jul 4, 2026
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.
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