AI-900 Practice Question: Describe features of computer vision workloads on Azure
A beverage company uses a camera system to inspect bottles on a conveyor belt. The system must automatically identify which bottles are defective (e.g., cracked or chipped) and which are acceptable, based on the overall appearance of each bottle. The company has thousands of labeled images of bottles (defective and non-defective). Which Azure Computer Vision service should they use to train a custom model?
⚠ Common exam trap
Many candidates confuse object detection with image classification, assuming that identifying defects requires bounding boxes, when the question only asks for overall bottle status (defective vs. acceptable) based on appearance.
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
✓
Custom Vision – Image classification
The scenario requires classifying each bottle image into one of two categories (defective or acceptable) based on overall appearance. Custom Vision – Image classification is designed exactly for this: it trains a model on labeled images to predict a single label per image, making it ideal for binary or multi-class classification tasks like defect detection.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Custom Vision – Object detection
Why it's wrong here
Object detection in Custom Vision localises multiple objects within an image by drawing bounding boxes around each one, but the scenario requires a single classification of the entire bottle as defective or acceptable based on overall appearance, not the detection of specific defects at pixel coordinates. It is tempting because object detection can identify cracks or chips as separate objects, and would be correct if the system needed to pinpoint each defect’s location on the bottle.
When this WOULD be correct
If the question required identifying and localizing specific defects (e.g., cracks or chips) within each bottle image, such as drawing bounding boxes around the defect, then Custom Vision – Object detection would be the correct choice.
- ✓
Custom Vision – Image classification
Why this is correct
Image classification in Custom Vision assigns a single label to the entire image, which is exactly the task of categorizing each bottle as defective or acceptable. The model learns from labeled photos of bottles that exhibit cracks, chips, or deformities versus intact ones, enabling a binary decision without needing to locate specific flaws. This fits the requirement that the system simply inspects bottles and flags defects.
- ✗
Optical Character Recognition (OCR)
Why it's wrong here
Optical Character Recognition (OCR) is designed to extract printed or handwritten text from images using the Azure AI Vision Read API. Bottle defects appear as physical surface anomalies, not textual characters, so OCR would return empty results or read label text instead of evaluating product quality. It lacks any capability to classify or detect visual defects, making it completely unsuitable for this inspection task.
When this WOULD be correct
A company needs to automatically read expiration dates or batch numbers printed on product labels from images of bottles on a conveyor belt. OCR would be the correct service to extract that text data.
- ✗
Face API
Why it's wrong here
The Face API is a pre-trained Azure service specialized for detecting human faces and analyzing attributes such as age, emotion, or facial landmarks. It cannot be retrained or customized to recognize bottle defects, and it would not return a classification like 'defective' because it expects a face presence. Using it for bottle inspection would yield irrelevant face data or no detection at all.
When this WOULD be correct
A company needs to identify employees entering a secure area by matching their faces against a database of authorized personnel. Face API would be used for facial recognition.
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The AI-900 exam frequently reuses these exact scenarios with slightly different constraints.
✓Custom Vision – Image classificationCorrect answer▾
Why this is correct
Image classification in Custom Vision assigns a single label to the entire image, which is exactly the task of categorizing each bottle as defective or acceptable. The model learns from labeled photos of bottles that exhibit cracks, chips, or deformities versus intact ones, enabling a binary decision without needing to locate specific flaws. This fits the requirement that the system simply inspects bottles and flags defects.
✗Custom Vision – Object detectionWrong answer — click to see why▾
Why this is wrong here
The task is to classify each bottle as defective or acceptable based on overall appearance, not to locate defects within the image. Object detection would be overkill and misaligned with the requirement of a single label per bottle.
★ When this WOULD be the correct answer
If the question required identifying and localizing specific defects (e.g., cracks or chips) within each bottle image, such as drawing bounding boxes around the defect, then Custom Vision – Object detection would be the correct choice.
Why candidates choose this
Candidates may confuse 'identifying defective bottles' with 'detecting defects in the image', assuming object detection is needed to find the defect location, even though the question only asks for a binary classification of the whole bottle.
✗Optical Character Recognition (OCR)Wrong answer — click to see why▾
Why this is wrong here
The question requires identifying defective vs. non-defective bottles based on overall appearance, which is a classification task, not text extraction. OCR is designed to extract text from images, not to classify objects or detect defects.
★ When this WOULD be the correct answer
A company needs to automatically read expiration dates or batch numbers printed on product labels from images of bottles on a conveyor belt. OCR would be the correct service to extract that text data.
Why candidates choose this
Candidates may mistakenly think OCR can be used for defect detection because it involves analyzing images, or they may confuse 'reading' defects with reading text.
✗Face APIWrong answer — click to see why▾
Why this is wrong here
Face API is designed for detecting and analyzing human faces, not for inspecting bottle defects based on overall appearance.
★ When this WOULD be the correct answer
A company needs to identify employees entering a secure area by matching their faces against a database of authorized personnel. Face API would be used for facial recognition.
Why candidates choose this
Candidates may think Face API can be used for any visual inspection task because it deals with images, but it is specialized for faces only.
Analysis generated from the official AI-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
Go deeper
Related to this question
Learn chapter
Regression and Classification
Key term
Label
A label is a piece of metadata attached to data, objects, or resources to identify, classify, or describe them for easier management and retrieval.
Key term
Computer vision
Computer vision is a field of artificial intelligence that enables computers to interpret and make decisions based on visual data from the world, such as images and videos.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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