- A
Dataset bias
The training set lacks lateral views, causing bias; the model has not learned to recognize features specific to lateral X-rays.
- B
Underfitting
Why wrong: Underfitting would result in poor performance on all data, not just lateral views.
- C
Label noise
Why wrong: Label noise would affect all images randomly, not systematically fail on a specific view type.
- D
Overfitting
Why wrong: Overfitting would cause poor performance on both frontal and lateral views if the test set was similar to training, but here performance is good on frontal only.
Question 606 of 1,000
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.
An AI model is being developed for medical diagnosis from X-ray images. The dataset contains only frontal chest X-rays. The model achieves high accuracy on test set but fails on lateral views. What is the most likely cause?
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
✓
Dataset bias
The model was trained exclusively on frontal chest X-rays, so it never learned features specific to lateral views. When tested on lateral views, the distribution shift causes poor performance, which is a classic case of dataset bias (sampling bias). The high accuracy on the test set is misleading because the test set also only contained frontal views, masking the model's inability to generalize to other X-ray orientations.
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.
- ✓
Dataset bias
Why this is correct
The training set lacks lateral views, causing bias; the model has not learned to recognize features specific to lateral X-rays.
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.
- ✗
Underfitting
Why it's wrong here
Underfitting would result in poor performance on all data, not just lateral views.
- ✗
Label noise
Why it's wrong here
Label noise would affect all images randomly, not systematically fail on a specific view type.
- ✗
Overfitting
Why it's wrong here
Overfitting would cause poor performance on both frontal and lateral views if the test set was similar to training, but here performance is good on frontal only.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between overfitting and dataset bias by presenting a scenario where the model performs well on the test set (which shares the same bias) but fails on a different data distribution, leading candidates to mistakenly choose overfitting instead of recognizing the sampling bias.
Trap categories for this question
Similar concept trap
Overfitting would cause poor performance on both frontal and lateral views if the test set was similar to training, but here performance is good on frontal only.
Detailed technical explanation
How to think about this question
Dataset bias occurs when the training data does not represent the full distribution of the real-world application. In medical imaging, this is critical: a model trained only on frontal chest X-rays will learn features like the cardiac silhouette and lung apex orientation specific to that view, but lateral views have different anatomical projections (e.g., retrosternal space, thoracic spine visibility). Real-world deployment would require either data augmentation with lateral views or domain adaptation techniques to handle covariate shift.
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
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 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: Dataset bias — The model was trained exclusively on frontal chest X-rays, so it never learned features specific to lateral views. When tested on lateral views, the distribution shift causes poor performance, which is a classic case of dataset bias (sampling bias). The high accuracy on the test set is misleading because the test set also only contained frontal views, masking the model's inability to generalize to other X-ray orientations.
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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Last reviewed: Jul 4, 2026
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