- A
Selection bias
Why wrong: Selection bias occurs when the data is not representative of the population, not specifically from historical prejudices.
- B
Algorithmic bias
Why wrong: Algorithmic bias is introduced by the model design or optimization, not solely from data.
- C
Confirmation bias
Why wrong: Confirmation bias affects how humans interpret model outputs, not the training data itself.
- D
Historical bias
Historical bias stems from data that mirrors past discriminatory practices.
Question 474 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 data scientist notices that a hiring model systematically scores female candidates lower than male candidates with similar qualifications. The training data was collected from past hiring decisions where the company historically hired more men. Which type of AI bias is most directly demonstrated?
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 correct answer is D, historical bias, because the model's lower scoring of female candidates stems directly from training data that reflects past hiring decisions where the company historically hired more men. This bias is embedded in the data itself, not introduced by the algorithm or data collection method. Historical bias occurs when the training data encodes societal or organizational prejudices from the past, which the model then perpetuates.
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.
- ✗
Selection bias
Why it's wrong here
Selection bias occurs when the data is not representative of the population, not specifically from historical prejudices.
- ✗
Algorithmic bias
Why it's wrong here
Algorithmic bias is introduced by the model design or optimization, not solely from data.
- ✗
Confirmation bias
Why it's wrong here
Confirmation bias affects how humans interpret model outputs, not the training data itself.
- ✓
Historical bias
Why this is correct
Historical bias stems from data that mirrors past discriminatory practices.
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 confuse 'algorithmic bias' as the catch-all term, missing that the root cause is the historical data, not the algorithm's logic.
Trap categories for this question
Command / output trap
Confirmation bias affects how humans interpret model outputs, not the training data itself.
Detailed technical explanation
How to think about this question
Historical bias is a form of data bias where the training labels reflect past discriminatory practices, such as a company's hiring decisions that favored men. Under the hood, the model learns correlations between features (e.g., gender) and outcomes (e.g., hire) from the training data, so if the data shows a lower hiring rate for women, the model will predict lower scores for female candidates even if qualifications are equal. In a real-world scenario, this can be mitigated by reweighing training samples or using techniques like adversarial debiasing to remove sensitive attribute correlations.
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 correct answer is D, historical bias, because the model's lower scoring of female candidates stems directly from training data that reflects past hiring decisions where the company historically hired more men. This bias is embedded in the data itself, not introduced by the algorithm or data collection method. Historical bias occurs when the training data encodes societal or organizational prejudices from the past, which the model then perpetuates.
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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