- A
Historical bias
Historical bias is baked into training data that reflects past discriminatory practices, such as gender imbalances in hiring data.
- B
Confirmation bias
Why wrong: Confirmation bias is a human cognitive bias where one favours information confirming pre-existing beliefs, not a data bias.
- C
Algorithmic bias
Why wrong: Algorithmic bias results from the model architecture or optimisation choices, not from biased training data.
- D
Selection bias
Why wrong: Selection bias would involve a non-representative sample of the population, not the reflection of historical societal biases.
Question 743 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.
A company deploys an AI resume screening tool. It learns from historical hiring data where most successful hires were male, leading the model to favour male candidates. Which type of bias is this primarily?
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
The model learned from historical hiring data that already contained a gender imbalance, where most successful hires were male. This is a classic case of historical bias, where the training data reflects past societal or organizational biases, and the AI system perpetuates those biases in its predictions. The bias originates in the data, not in the model's algorithm or the sampling method.
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.
- ✓
Historical bias
Why this is correct
Historical bias is baked into training data that reflects past discriminatory practices, such as gender imbalances in hiring data.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Confirmation bias
Why it's wrong here
Confirmation bias is a human cognitive bias where one favours information confirming pre-existing beliefs, not a data bias.
- ✗
Algorithmic bias
Why it's wrong here
Algorithmic bias results from the model architecture or optimisation choices, not from biased training data.
- ✗
Selection bias
Why it's wrong here
Selection bias would involve a non-representative sample of the population, not the reflection of historical societal biases.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between historical bias and algorithmic bias, where candidates mistakenly attribute the problem to the algorithm itself rather than recognizing that the bias was already present in the training data.
Detailed technical explanation
How to think about this question
Historical bias is a form of data bias that arises when the training dataset encodes existing societal inequalities, such as gender or racial disparities in hiring. Under the hood, the model minimizes a loss function (e.g., cross-entropy) and learns correlations between features (e.g., gender) and labels (e.g., 'hired') without understanding fairness constraints. In real-world deployments, this can lead to disparate impact, violating regulations like the EU AI Act or EEOC guidelines, and requires techniques like reweighting, adversarial debiasing, or fairness-aware regularization to mitigate.
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 — The model learned from historical hiring data that already contained a gender imbalance, where most successful hires were male. This is a classic case of historical bias, where the training data reflects past societal or organizational biases, and the AI system perpetuates those biases in its predictions. The bias originates in the data, not in the model's algorithm or the sampling method.
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
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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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