The correct answer is measurement bias and selection bias. Measurement bias occurs when systematic errors in how features are recorded—such as inconsistent data collection methods across customer segments—skew predictions and harm fairness, while selection bias arises when the training data fails to represent the true customer population, for example by using only data from a specific time period or region, causing the model to generalize poorly. On the CompTIA AI+ AI0-001 exam, this question tests your ability to distinguish between bias types that affect both fairness and accuracy in supervised learning; a common trap is confusing selection bias with sampling bias, but remember that selection bias is about non-representative data collection, whereas measurement bias is about flawed feature recording. To keep them straight, think: selection is about who you pick, measurement is about how you measure.
AI0-001 AI Concepts and Foundations Practice Question
This AI0-001 practice question tests your understanding of ai concepts and foundations. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 is training a supervised learning model for customer churn prediction. Which TWO types of bias are most likely to affect the model's fairness and accuracy if not addressed?
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 the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Selection bias
Selection bias (B) occurs when the training data does not represent the true customer population, e.g., using only data from a specific time period or region, leading to a model that fails to generalize. Measurement bias (C) arises from systematic errors in how features are recorded, such as inconsistent data collection methods across customer segments, which can skew predictions and harm fairness.
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 is a consequence of biased data or design, not a type of data bias.
✓
Selection bias
Why this is correct
Selection bias arises when the sample is not representative of the population, leading to skewed predictions.
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.
✓
Measurement bias
Why this is correct
Measurement bias occurs when data collection methods systematically misrepresent true values.
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.
✗
Sampling bias
Why it's wrong here
Sampling bias is a subset of selection bias; the question expects distinct types.
✗
Confirmation bias
Why it's wrong here
Confirmation bias is a human tendency, not a data bias.
Common exam traps
Common exam trap: answer the scenario, not the keyword
CompTIA often tests the distinction between data-level biases (selection, measurement) and human cognitive biases (confirmation bias), so candidates mistakenly pick confirmation bias because it sounds plausible in a data science context.
Detailed technical explanation
How to think about this question
Selection bias often manifests as 'survivorship bias' in churn models when only current customers are used for training, ignoring those who already churned. Measurement bias can be introduced by differing data collection frequencies (e.g., daily vs. weekly logs) across customer tiers, causing the model to learn spurious correlations. In practice, these biases degrade the model's calibration and lead to unequal error rates across demographic groups, violating fairness constraints.
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.
AI Concepts and Foundations — This question tests AI Concepts and Foundations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Selection bias — Selection bias (B) occurs when the training data does not represent the true customer population, e.g., using only data from a specific time period or region, leading to a model that fails to generalize. Measurement bias (C) arises from systematic errors in how features are recorded, such as inconsistent data collection methods across customer segments, which can skew predictions and harm fairness.
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.
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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.
Question Discussion
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