AI-900 Practice Question: Describe features of computer vision workloads on Azure
A quality control manager at a bottling plant needs an automated system to inspect images of bottles coming off the production line. The system must determine whether each bottle has a correctly sealed cap or is defective (cap missing or crooked). The manager has a set of labeled images showing both acceptable and defective bottles. Which Azure Computer Vision service should they use to build a model that classifies each bottle image as 'acceptable' or 'defective'?
⚠ Common exam trap
Watch out — candidates often confuse Azure Custom Vision with Azure OCR or Form Recognizer because all three involve image analysis, but only Custom Vision allows training a custom classifier for non-text visual features like bottle cap integrity.
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
✓
Azure Custom Vision (Image Classification)
Azure Custom Vision (Image Classification) is the correct service because it allows you to upload labeled images of bottles (acceptable and defective) and train a custom image classification model to distinguish between the two classes. This service is specifically designed for scenarios where you need to classify images into user-defined categories without requiring deep learning expertise.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Azure Face API
Why it's wrong here
Azure Face API specializes in detecting, identifying, and analyzing human faces, including attributes like emotion and age, as well as face comparison for identity verification. It is not a general-purpose object classifier and cannot recognize bottle caps or evaluate manufacturing quality. Using Face API for this task would be a domain mismatch, as it is optimized for facial data, not arbitrary industrial objects.
When this WOULD be correct
A question asks for a service to detect if a person is wearing a hard hat in a factory image, using a pre-built model that can identify faces and accessories. Azure Face API with attributes like 'accessories' would be the correct choice.
- ✓
Azure Custom Vision (Image Classification)
Why this is correct
Azure Custom Vision (Image Classification) is the correct choice because it enables you to upload a labeled dataset of bottle cap images and train a custom model that outputs a categorical prediction, such as 'acceptable' or 'defective'. As part of Azure Cognitive Services, it leverages transfer learning from pre-trained neural networks, so even a small number of examples can produce a usable classifier without requiring you to build a machine learning pipeline from scratch. This directly fulfills the automated visual quality-control requirement.
- ✗
Azure Form Recognizer
Why it's wrong here
Azure Form Recognizer is a document-processing service that extracts structured data from forms, invoices, receipts, and similar documents using layout analysis and key-value pair extraction. It does not perform object classification or detect physical defects in manufacturing items. Since the task is to classify bottle caps visually rather than parse a document, Form Recognizer is not applicable.
When this WOULD be correct
A question asks for a service to automatically extract key-value pairs and tables from scanned purchase orders or invoices, where the documents have varied layouts but contain similar fields.
- ✗
Azure OCR (Read API)
Why it's wrong here
Azure OCR (Read API) is designed specifically for optical character recognition, pulling printed and handwritten text out of images and converting it into machine-readable text. It cannot judge the condition of a bottle cap or distinguish acceptable from defective items, as it ignores all non-text visual features. While it might read text printed on a label, it provides no information about the cap's physical integrity.
When this WOULD be correct
A scenario where the system must read and verify text on bottle labels (e.g., expiration date, batch number) to check for printing errors or missing information would make Azure OCR the correct choice.
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.
✓Azure Custom Vision (Image Classification)Correct answer▾
Why this is correct
Azure Custom Vision (Image Classification) is the correct choice because it enables you to upload a labeled dataset of bottle cap images and train a custom model that outputs a categorical prediction, such as 'acceptable' or 'defective'. As part of Azure Cognitive Services, it leverages transfer learning from pre-trained neural networks, so even a small number of examples can produce a usable classifier without requiring you to build a machine learning pipeline from scratch. This directly fulfills the automated visual quality-control requirement.
✗Azure Face APIWrong answer — click to see why▾
Why this is wrong here
Azure Face API is designed for detecting, analyzing, and recognizing human faces, not for classifying industrial objects like bottle caps. It cannot be trained on custom images of bottles to distinguish acceptable vs. defective caps.
★ When this WOULD be the correct answer
A question asks for a service to detect if a person is wearing a hard hat in a factory image, using a pre-built model that can identify faces and accessories. Azure Face API with attributes like 'accessories' would be the correct choice.
Why candidates choose this
Candidates may think 'Face API' can be generalized to any visual inspection task because it deals with images, or they confuse it with Custom Vision's image classification capabilities.
✗Azure Form RecognizerWrong answer — click to see why▾
Why this is wrong here
Azure Form Recognizer is designed for extracting structured data from documents (e.g., invoices, forms), not for classifying images of bottles as acceptable or defective based on visual features like cap presence.
★ When this WOULD be the correct answer
A question asks for a service to automatically extract key-value pairs and tables from scanned purchase orders or invoices, where the documents have varied layouts but contain similar fields.
Why candidates choose this
Candidates may confuse 'form' with 'quality control forms' or think that any document processing service can handle image classification tasks.
✗Azure OCR (Read API)Wrong answer — click to see why▾
Why this is wrong here
Azure OCR (Read API) extracts text from images, but the task requires classifying bottle caps as acceptable or defective based on visual features, not text recognition.
★ When this WOULD be the correct answer
A scenario where the system must read and verify text on bottle labels (e.g., expiration date, batch number) to check for printing errors or missing information would make Azure OCR the correct choice.
Why candidates choose this
Candidates may confuse OCR with image classification because both involve analyzing images, but OCR focuses on text extraction rather than visual defect detection.
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
Machine Learning Core Concepts
Key term
Service
A service is a software component or system that performs a specific function and is available to be used by other programs or users over a network.
Key term
Classification
Classification is a supervised machine learning technique used to predict a category or class label for new data based on patterns learned from labeled training data.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-900 exam.