- A
The training set does not include enough negative examples that look like dogs but are not.
Lack of hard negatives causes false positives.
- B
The probability threshold is set too low.
Why wrong: Threshold affects precision/recall but not the root cause.
- C
The model is overfitted to the training data.
Why wrong: Overfitting would cause high training accuracy but low test accuracy, but test was fine.
- D
The training set has class imbalance.
Why wrong: Both classes have equal images.
Question 905 of 993
AI-102 Implement computer vision solutions Practice Question
This AI-102 practice question tests your understanding of implement computer vision solutions. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 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
✓
The training set does not include enough negative examples that look like dogs but are not.
The model misclassifies wolves as dogs because the training set lacks negative examples that are visually similar to dogs but belong to a different class. Custom Vision learns to distinguish classes based on the features present in the training images; without images of wolf-like canines labeled as 'not dog,' the model has no basis to reject wolves. This is a classic case of insufficient hard negative mining, where the model generalizes too broadly for the 'dog' class.
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
Microsoft often tests the misconception that class imbalance is the primary cause of misclassification, but here the dataset is balanced, and the real issue is the lack of representative negative examples—a subtle but critical distinction in Custom Vision training.
Detailed technical explanation
How to think about this question
Under the hood, Azure Custom Vision uses a convolutional neural network (CNN) that extracts hierarchical features; without negative examples that share low-level features (e.g., fur texture, snout shape) with the target class, the decision boundary for 'dog' becomes too inclusive. In real-world scenarios, this is mitigated by adding 'difficult negatives'—images that are not dogs but resemble them, such as wolves, coyotes, or foxes—to the training set, often through iterative retraining cycles. The model's softmax output assigns probabilities across classes, but if the 'dog' class dominates the feature space for canid shapes, wolves will receive a high 'dog' score.
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 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.
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FAQ
Questions learners often ask
What does this AI-102 question test?
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. — The model misclassifies wolves as dogs because the training set lacks negative examples that are visually similar to dogs but belong to a different class. Custom Vision learns to distinguish classes based on the features present in the training images; without images of wolf-like canines labeled as 'not dog,' the model has no basis to reject wolves. This is a classic case of insufficient hard negative mining, where the model generalizes too broadly for the 'dog' class.
What should I do if I get this AI-102 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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