AI-102 Plan and manage an Azure AI solution Practice Question
This AI-102 practice question tests your understanding of plan and manage an azure ai solution. 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 responsible for an Azure AI solution that uses Custom Vision to classify manufacturing defects. The model must achieve high recall to avoid missing defects. The current model has high precision but low recall. Which action should you take?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Lower the probability threshold for the defect class
Lowering the probability threshold for the defect class means the model will classify an image as defective even when its confidence score is lower. This increases the number of true positives (defects caught), directly improving recall at the cost of potentially more false positives. In Custom Vision, the default probability threshold is 50%, and adjusting it downward is the standard technique to prioritize recall over precision.
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 more images of non-defective items to the training set
Why it's wrong here
Adding negatives improves precision, not recall.
✗
Increase the number of training iterations
Why it's wrong here
More iterations may not specifically improve recall.
✓
Lower the probability threshold for the defect class
Why this is correct
Lowering the threshold increases the number of positive predictions, thus improving recall.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the probability threshold for the defect class
Why it's wrong here
Increasing the threshold reduces false positives but also reduces recall.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse precision and recall, often assuming that increasing the threshold (making the model stricter) will improve overall performance, when in fact it reduces recall by missing more defects.
Detailed technical explanation
How to think about this question
The probability threshold in Custom Vision acts as a decision boundary on the softmax output of the neural network. By default, it is set to 50%, meaning only predictions with a confidence score above 50% are accepted. Lowering this threshold to, say, 30% captures more borderline defect cases, directly increasing recall. In practice, you can tune this threshold using the model's precision-recall curve, which is available in the Custom Vision portal, to find the optimal balance for your specific recall requirement.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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.
Plan and manage an Azure AI solution — This question tests Plan and manage an Azure AI solution — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Lower the probability threshold for the defect class — Lowering the probability threshold for the defect class means the model will classify an image as defective even when its confidence score is lower. This increases the number of true positives (defects caught), directly improving recall at the cost of potentially more false positives. In Custom Vision, the default probability threshold is 50%, and adjusting it downward is the standard technique to prioritize recall over precision.
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.
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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