The answer is Azure Custom Vision, specifically its Image Classification capability. This service is correct because it enables you to upload your labeled dataset of bottle cap images—both acceptable and defective—and train a custom model to distinguish between the two classes without needing deep learning expertise. For the AI-900 exam, this scenario tests your understanding of when to use Custom Vision versus pre-built Computer Vision services like the Image Analysis API, which cannot be trained on your specific quality control categories. A common trap is confusing Custom Vision with the pre-built Optical Character Recognition or object detection services; remember that Custom Vision is for training on your own labeled images to create a binary or multi-class classifier. Memory tip: think “Custom” for your own categories—if you have labeled images of your specific defects, Custom Vision is the tool to train a model that classifies them as acceptable or defective.
AI-900 Practice Question: Describe features of computer vision workloads on Azure
This AI-900 practice question tests your understanding of describe features of computer vision workloads on azure. 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.
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'?
Why wrong: Azure Face API is designed for detecting and recognizing human faces, not for classifying arbitrary objects like bottle caps.
B
Azure Custom Vision (Image Classification)
Custom Vision enables you to train a custom image classifier using your own labeled dataset, which is exactly what is needed to distinguish acceptable bottles from defective ones.
C
Azure Form Recognizer
Why wrong: Form Recognizer specializes in extracting text and structure from forms and documents, not in classifying images of objects.
D
Azure OCR (Read API)
Why wrong: The Read API is used for extracting printed and handwritten text from images, not for classifying the content of images into categories.
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.
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.
✗
Azure Face API
Why it's wrong here
Azure Face API is designed for detecting and recognizing human faces, not for classifying arbitrary objects like bottle caps.
✓
Azure Custom Vision (Image Classification)
Why this is correct
Custom Vision enables you to train a custom image classifier using your own labeled dataset, which is exactly what is needed to distinguish acceptable bottles from defective ones.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure Form Recognizer
Why it's wrong here
Form Recognizer specializes in extracting text and structure from forms and documents, not in classifying images of objects.
✗
Azure OCR (Read API)
Why it's wrong here
The Read API is used for extracting printed and handwritten text from images, not for classifying the content of images into categories.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may 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.
Detailed technical explanation
How to think about this question
Azure Custom Vision uses transfer learning on a pre-trained convolutional neural network (CNN) to adapt to your custom dataset, requiring only a few dozen labeled images per class for reasonable accuracy. The service supports both image classification (single-label or multi-label) and object detection, and you can export the trained model as a Docker container for edge deployment on the production line. A subtle behavior is that the model's confidence threshold can be tuned to balance false positives (e.g., marking a good bottle as defective) versus false negatives, which is critical in quality control to avoid unnecessary rework.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
Describe features of computer vision workloads on Azure — This question tests Describe features of computer vision workloads on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this AI-900 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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