- A
Add a confidence score disclaimer to model outputs
Why wrong: Explanation alone does not correct the bias; the model needs retraining.
- B
Reduce the sample size of non-native English speakers to balance the dataset
Why wrong: Reducing data can increase bias and reduce model performance.
- C
Continue using the model as is, since overall accuracy is acceptable
Why wrong: Ignoring biased outcomes is unethical and could lead to disparate impact.
- D
Retrain the model with a more representative dataset that includes diverse language backgrounds
Retraining with balanced data addresses the root cause of bias.
Question 517 of 1,000
Bias Mitigation by Retraining with Representative Data
This AI0-001 practice question tests your understanding of ai security, ethics and governance. 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 healthcare startup deploys an AI model to predict patient readmission rates. An internal audit reveals that the model consistently underestimates readmission risk for non-native English speakers. According to AI ethics principles, what is the most appropriate course of action?
Quick Answer
The correct answer is to retrain the model with a more representative dataset that includes diverse language backgrounds. This is the most appropriate course of action because the bias stems directly from unrepresentative training data; bias mitigation by retraining with representative data addresses the root cause by ensuring the model learns from a balanced distribution of patient demographics, including non-native English speakers. On the CompTIA AI+ AI0-001 exam, this scenario tests your understanding of the AI ethics principle of fairness and the practical step of data-level bias correction. A common trap is choosing a post-hoc explanation method, which only describes bias without fixing it, or reducing sample size, which can amplify disparities. Remember the memory tip: “Fix the data, not the output”—if the training set is skewed, retraining with balanced, representative data is the only ethical and effective remedy.
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
✓
Retrain the model with a more representative dataset that includes diverse language backgrounds
Option D is correct because it directly addresses the root cause of the bias: the training data lacks sufficient representation from non-native English speakers, leading to systematic underestimation of readmission risk for that group. Retraining with a more representative dataset aligns with the AI ethics principle of fairness by ensuring the model learns patterns across all demographic groups equally, rather than masking the issue with disclaimers or manipulating sample sizes.
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.
- ✗
Add a confidence score disclaimer to model outputs
Why it's wrong here
Explanation alone does not correct the bias; the model needs retraining.
- ✗
Reduce the sample size of non-native English speakers to balance the dataset
Why it's wrong here
Reducing data can increase bias and reduce model performance.
- ✗
Continue using the model as is, since overall accuracy is acceptable
Why it's wrong here
Ignoring biased outcomes is unethical and could lead to disparate impact.
- ✓
Retrain the model with a more representative dataset that includes diverse language backgrounds
Why this is correct
Retraining with balanced data addresses the root cause of 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 misconception that adding a disclaimer or adjusting sample sizes post-hoc is sufficient to address bias, when in fact the ethical requirement is to fix the data or model at the training stage to ensure fairness.
Detailed technical explanation
How to think about this question
In practice, this bias often arises from imbalanced training data where the model learns spurious correlations (e.g., associating certain phrasing patterns with lower risk) due to insufficient examples from minority language groups. Techniques like stratified sampling or reweighting during training can help, but the most robust solution is to collect or augment data to ensure proportional representation across all relevant demographics. Real-world cases, such as biased hospital readmission models, have shown that ignoring such disparities can lead to regulatory penalties and loss of trust in AI systems.
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 network engineer segments a warehouse floor into three subnets: 20 scanners, 5 printers, and 2 management hosts. Picking the wrong mask wastes addresses or leaves too few usable hosts. Exam questions test whether you can apply CIDR notation, calculate block size, and identify the correct usable-host range for a given prefix.
What to study next
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FAQ
Questions learners often ask
What does this AI0-001 question test?
AI Security, Ethics and Governance — This question tests AI Security, Ethics and Governance — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Retrain the model with a more representative dataset that includes diverse language backgrounds — Option D is correct because it directly addresses the root cause of the bias: the training data lacks sufficient representation from non-native English speakers, leading to systematic underestimation of readmission risk for that group. Retraining with a more representative dataset aligns with the AI ethics principle of fairness by ensuring the model learns patterns across all demographic groups equally, rather than masking the issue with disclaimers or manipulating sample sizes.
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.
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Last reviewed: Jul 4, 2026
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