- A
Confirmation bias
Why wrong: Confirmation bias is a cognitive bias where humans favor information that confirms their preexisting beliefs; it is not a data bias.
- B
Selection bias
Why wrong: Selection bias arises when the training data is not representative of the population; the scenario describes disparate impact likely due to historical patterns in loan data.
- C
Algorithmic bias
Why wrong: Algorithmic bias is a general term; the specific source here is historical bias in the data.
- D
Historical bias
Historical bias is present when the training data encodes past societal biases, which the model then amplifies.
Question 320 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 discovers that a model trained to predict loan defaults is denying loans at a higher rate for a particular demographic group. Which type of bias is MOST likely present?
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
✓
Historical bias
Historical bias occurs when the training data reflects past societal inequalities, leading the model to learn and perpetuate those patterns. In this case, if historical loan data shows higher denial rates for a demographic group due to past discriminatory practices, the model will replicate that bias in its predictions. This is the most likely cause because the model is not inherently biased but inherits bias from the data it was trained on.
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.
- ✗
Confirmation bias
Why it's wrong here
Confirmation bias is a cognitive bias where humans favor information that confirms their preexisting beliefs; it is not a data bias.
- ✗
Selection bias
Why it's wrong here
Selection bias arises when the training data is not representative of the population; the scenario describes disparate impact likely due to historical patterns in loan data.
- ✗
Algorithmic bias
Why it's wrong here
Algorithmic bias is a general term; the specific source here is historical bias in the data.
- ✓
Historical bias
Why this is correct
Historical bias is present when the training data encodes past societal biases, which the model then amplifies.
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse 'algorithmic bias' (a general term) with the specific root cause, failing to recognize that historical bias is the precise type when the bias originates from the training data rather than the algorithm itself.
Trap categories for this question
Scenario analysis trap
Selection bias arises when the training data is not representative of the population; the scenario describes disparate impact likely due to historical patterns in loan data.
Detailed technical explanation
How to think about this question
Historical bias is a form of data bias where the training set contains systemic prejudices from past decisions, such as redlining in lending. Under the hood, the model learns correlations between demographic features and loan outcomes, even if those correlations are spurious or unethical. In real-world scenarios, this can lead to regulatory violations under laws like the Equal Credit Opportunity Act (ECOA) if not mitigated through fairness-aware machine learning techniques.
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 security administrator must allow nursing staff to reach a patient records server while blocking access from the guest Wi-Fi VLAN. After applying an extended ACL, traffic is still blocked from nursing workstations. The ACL was applied outbound instead of inbound on the wrong interface. Questions like this test ACL direction and placement rules.
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 occurs when the training data reflects past societal inequalities, leading the model to learn and perpetuate those patterns. In this case, if historical loan data shows higher denial rates for a demographic group due to past discriminatory practices, the model will replicate that bias in its predictions. This is the most likely cause because the model is not inherently biased but inherits bias from the data it was trained on.
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.
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 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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