hardMultiple Choice
AI-102 Practice Question: The Azure AI engineer for a large e-commerce…
You are the Azure AI engineer for a large e-commerce company. The company uses Azure Computer Vision to automatically tag product images uploaded by sellers. The system has been running smoothly for months. However, after a recent update to the Computer Vision API, you notice that certain images of clothing items are being tagged with incorrect labels, such as 'shoe' for a shirt. The images are clear and well-lit. You have confirmed that the image format (JPEG) is supported and the size is within limits. The issue occurs consistently for clothing items with similar colors. Other product categories work fine. You suspect the issue is related to the API version. What should you do first?
⚠ Common exam trap
The trap here is that candidates may jump to retraining a custom model (Option C) or adjusting confidence thresholds (Option D) without first verifying the API version, which is the simplest and most cost-effective diagnostic step in Azure AI troubleshooting.
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
✓
Check the API version used in the application code and compare with the latest version.
The issue began after a Computer Vision API update, and the problem is specific to certain clothing images with similar colors, indicating a potential regression or behavioral change in the API version. Checking the API version used in the application code against the latest version is the first logical troubleshooting step to identify if a breaking change or bug was introduced. This aligns with Azure AI best practices: always verify API version compatibility before modifying thresholds or retraining models.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the image size limit.
Why it's wrong here
Image size limits govern whether an image is accepted for analysis at all; exceeding them returns an error, not a wrong label. The images are already within limits, so raising the cap changes nothing. Size limits would be relevant when uploads are rejected for being too large.
- ✓
Check the API version used in the application code and compare with the latest version.
Why this is correct
Version-specific model changes can alter labelling behaviour, so verifying the API version in code against the latest release isolates whether the regression stems from the update. This is the first diagnostic step before retraining or altering image preprocessing.
- ✗
Switch to a custom model trained on clothing items.
Why it's wrong here
A custom model is trained on your own labelled images to recognise domain-specific classes the prebuilt model lacks. Here the prebuilt tagging worked for months and broke after an API update, so the regression lies in the service version, not model coverage. Custom training would be correct when prebuilt categories genuinely do not exist.
- ✗
Reduce the confidence threshold to 50% to see if more tags appear.
Why it's wrong here
Lowering the confidence threshold makes the service return more candidate tags, including weaker ones; it does not correct a label that is confidently wrong. The mislabelling stems from the API version change, so threshold tuning adds noise. Threshold adjustment would be correct when valid tags are being suppressed for falling just below the cutoff.
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