AI-102 Implement computer vision solutions Practice Question
A company is building a computer vision solution using Azure AI Vision to analyze images of retail shelves. The solution must detect product presence and read expiration dates. Which TWO Azure AI Vision features should be used?
⚠ Common exam trap
Microsoft Azure often tests the distinction between object detection and image classification or captioning, where candidates mistakenly choose image captioning for product presence instead of object detection, which provides precise localization and identification.
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
✓
Object detection
Object detection (C) is correct because it locates and classifies multiple objects within an image, which is exactly what is needed to determine whether specific products are present on retail shelves. Optical Character Recognition (D) is correct because OCR extracts printed or handwritten text from images, enabling the solution to read expiration dates printed on product packaging. Face detection (A) only identifies human faces and their attributes, which is irrelevant to detecting products or reading dates. Brand detection (B) identifies known company logos but does not determine product presence or read expiration dates. Image captioning (E) generates a natural-language description of an image and cannot reliably detect specific products or extract date text.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Face detection
Why it's wrong here
Face detection returns bounding boxes and attributes for human faces, so it cannot identify products or read expiry text on shelves. It is tempting because it is a core Azure AI Vision feature, and it would be correct for scenarios such as access control, people counting, or blurring faces in published media.
- ✗
Brand detection
Why it's wrong here
Brand detection recognises known company logos from a fixed model, so it cannot confirm arbitrary product presence or read expiry dates. It is tempting because it targets retail imagery, and it would be correct for measuring shelf share or logo visibility for brands already supported by the detector.
- ✓
Object detection
Why this is correct
Object detection returns bounding boxes with labels for each product on the shelf, directly satisfying the product-presence requirement. Unlike image classification, which assigns a single label per image, detection localises multiple distinct items, so the solution can confirm which products are present and where before reading their expiration dates.
- ✓
Optical Character Recognition (OCR)
Why this is correct
Optical Character Recognition extracts printed or handwritten text from images, directly satisfying the requirement to read expiration dates on product packaging. Azure AI Vision's Read API handles dense, small text typical of date stamps, returning recognised characters and bounding boxes that downstream logic can parse into expiry values.
- ✗
Image captioning
Why it's wrong here
Image captioning generates a natural-language description of the whole scene, not per-product presence or expiry date text. It is tempting as a headline Azure AI Vision capability, and it would be the right choice for accessibility alt text, image indexing, or summarising photo content rather than shelf-level OCR.
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