- A
Algorithmic bias
Why wrong: Algorithmic bias would stem from model design choices, not from the training data itself.
- B
Confirmation bias
Why wrong: Confirmation bias is a human cognitive bias, not a data bias.
- C
Selection bias
Why wrong: Selection bias results from a non-representative sample, not from historical inequalities in the data.
- D
Historical bias
The data reflects past discriminatory hiring practices, causing the model to perpetuate that bias.
Question 1,000 of 1,000
AI0-001 AI Governance and Ethics Practice Question
This AI0-001 practice question tests your understanding of ai governance and ethics. 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.
An AI team notices that their hiring model consistently selects male candidates over equally qualified female candidates. Analysis shows the training data contains past hiring decisions where men were predominantly hired. Which type of bias is the root cause?
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
✓
Historical bias
Historical bias is the root cause because the training data reflects past hiring decisions that systematically favored male candidates, encoding societal or organizational prejudices into the model. The model learns these historical patterns and perpetuates them, leading to discriminatory outcomes against equally qualified female candidates. This is distinct from algorithmic bias, which would arise from the model's design or optimization process 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.
- ✗
Algorithmic bias
Why it's wrong here
Algorithmic bias would stem from model design choices, not from the training data itself.
- ✗
Confirmation bias
Why it's wrong here
Confirmation bias is a human cognitive bias, not a data bias.
- ✗
Selection bias
Why it's wrong here
Selection bias results from a non-representative sample, not from historical inequalities in the data.
- ✓
Historical bias
Why this is correct
The data reflects past discriminatory hiring practices, causing the model to perpetuate that bias.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between historical bias (data-driven) and algorithmic bias (model-driven), and the trap here is that candidates may confuse the source of bias as being from the algorithm itself rather than the training data.
Detailed technical explanation
How to think about this question
Historical bias is a form of data bias where the training dataset contains outcomes that reflect past societal or organizational prejudices, such as gender discrimination in hiring. Under the hood, the model learns correlations between features (e.g., gender) and labels (e.g., hire decision) from the data, and if the data is skewed, the model will replicate those correlations. In real-world scenarios, this can lead to feedback loops where biased predictions reinforce the original bias, as seen in resume screening tools that penalized female candidates based on historical hiring patterns.
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 AI0-001 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 AI0-001 question test?
AI Governance and Ethics — This question tests AI Governance and Ethics — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Historical bias — Historical bias is the root cause because the training data reflects past hiring decisions that systematically favored male candidates, encoding societal or organizational prejudices into the model. The model learns these historical patterns and perpetuates them, leading to discriminatory outcomes against equally qualified female candidates. This is distinct from algorithmic bias, which would arise from the model's design or optimization process itself.
What should I do if I get this AI0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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 AI0-001 practice question is part of Courseiva's free CompTIA 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 AI0-001 exam.
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