The answer is that the training set does not include enough negative examples that look like dogs but are not. This is the most likely cause of the custom vision misclassification because the model learned to associate all large, wolf-like animals with the "dog" class, lacking the diverse negative samples needed to distinguish a wolf from a dog. On the Microsoft Azure AI Engineer Associate AI-102 exam, this scenario tests your understanding of training data diversity and how missing representative negative examples leads to false positives in production, even when class balance is perfect. A common trap is to blame class imbalance, but here both classes have 1000 images, so the real issue is insufficient visual variety in the negative class. Remember the memory tip: "If it looks like a dog but isn't, your training set needs more 'wolf-like' negatives."
AI-102 Implement computer vision solutions Practice Question
This AI-102 practice question tests your understanding of implement computer vision solutions. 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.
You are using Azure AI Custom Vision to classify images of animals. The training set has 1000 images of cats and 1000 images of dogs. After training, the model performs well on the test set. However, when deployed, it misclassifies images of wolves as dogs. 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 the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The training set does not include enough negative examples that look like dogs but are not.
Option B is correct because the training set likely lacks sufficient representative images of wolves, so the model learned that all large, wolf-like animals are dogs. Class imbalance (A) is not an issue here since both classes have 1000 images. Overfitting (C) would show poor performance on test set. Probability threshold (D) is not the root cause.
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.
✓
The training set does not include enough negative examples that look like dogs but are not.
Why this is correct
Lack of hard negatives causes false positives.
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.
✗
The probability threshold is set too low.
Why it's wrong here
Threshold affects precision/recall but not the root cause.
✗
The model is overfitted to the training data.
Why it's wrong here
Overfitting would cause high training accuracy but low test accuracy, but test was fine.
✗
The training set has class imbalance.
Why it's wrong here
Both classes have equal images.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which AI-102 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Implement computer vision solutions — This question tests Implement computer vision solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The training set does not include enough negative examples that look like dogs but are not. — Option B is correct because the training set likely lacks sufficient representative images of wolves, so the model learned that all large, wolf-like animals are dogs. Class imbalance (A) is not an issue here since both classes have 1000 images. Overfitting (C) would show poor performance on test set. Probability threshold (D) is not the root cause.
What should I do if I get this AI-102 question wrong?
Identify which AI-102 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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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Question Discussion
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