The answer is to use Custom Vision to train a classification or object detection model with transfer learning. This is correct because transfer learning allows a pre-trained neural network—already capable of recognizing general shapes and textures—to be fine-tuned on your small labeled dataset of defective and non-defective parts, making it highly effective for transfer learning custom vision small dataset scenarios. On the AI-900 exam, this question tests your understanding of how Azure Custom Vision leverages transfer learning to overcome data scarcity, especially when images vary in lighting and angle. A common trap is to assume you need a massive dataset or to build a model from scratch, but the exam emphasizes that transfer learning is the go-to solution for limited data. Memory tip: think “Custom Vision = Customize with Transfer” to recall that pre-trained power plus your small data equals a fast, accurate defect detector.
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 manufacturing company wants to use Azure AI to detect surface defects on metal parts. The team has a small set of labeled images of defective and non-defective parts, and images will be taken under various lighting conditions and angles. They need a solution that can leverage a pre-trained model and adapt it to their specific defect types with minimal new training data. Which approach should they take?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
A. Use Custom Vision to train a classification or object detection model with transfer learning
Option A is correct because Custom Vision allows you to use transfer learning, which starts from a pre-trained model and fine-tunes it on your small labeled dataset of defective and non-defective parts. This approach is ideal when you have limited training data and need to adapt the model to specific defect types under varying lighting and angles, as Custom Vision supports both classification and object detection for surface defects.
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.
✓
A. Use Custom Vision to train a classification or object detection model with transfer learning
Why this is correct
Correct. Custom Vision uses transfer learning from pre-trained models, enabling effective training with a small dataset to detect specific defects.
Related concept
Read the scenario before looking for a memorised answer.
✗
B. Use the Optical Character Recognition (OCR) API
Why it's wrong here
OCR extracts printed text from images; it is not designed to detect object defects based on visual patterns.
✗
C. Use the Describe Image API (Image Captioning)
Why it's wrong here
The Describe Image API generates human-readable descriptions of image content; it does not perform defect detection or classification.
✗
D. Use the Face API
Why it's wrong here
Face API is specialized for detecting and analyzing human faces; it cannot detect defects on metal parts.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse the general-purpose image analysis APIs (OCR, captioning, face) with Custom Vision's specialized ability to train custom models using transfer learning, assuming any Azure AI service can be adapted to a custom task without understanding the underlying training mechanism.
Detailed technical explanation
How to think about this question
Transfer learning in Custom Vision works by taking a convolutional neural network (CNN) pre-trained on large datasets like ImageNet and retraining only the final layers on your custom images, which dramatically reduces the amount of labeled data needed. The service also supports domain-specific fine-tuning (e.g., 'General' vs. 'Food' vs. 'Landmarks') and can handle variations in lighting and angles through data augmentation during training. In a real-world scenario, a manufacturer might use Custom Vision's object detection mode to both classify and localize defects, enabling automated quality control on a production line.
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
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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: A. Use Custom Vision to train a classification or object detection model with transfer learning — Option A is correct because Custom Vision allows you to use transfer learning, which starts from a pre-trained model and fine-tunes it on your small labeled dataset of defective and non-defective parts. This approach is ideal when you have limited training data and need to adapt the model to specific defect types under varying lighting and angles, as Custom Vision supports both classification and object detection for surface defects.
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.
About these practice questions
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Variation 1. What is 'few-shot learning' in the context of Azure AI Custom Vision model training?
hard
A.Training a model using only a small subset of available compute resources
✓ B.Training an accurate vision model with very few labelled examples using transfer learning
C.A technique for running multiple small training experiments in parallel
D.Limiting training to the first few hundred iterations regardless of convergence
Why B: Few-shot learning in Azure AI Custom Vision refers to training an accurate vision model with very few labeled examples by leveraging transfer learning. This approach uses a pre-trained neural network (e.g., ResNet) as a starting point, allowing the model to learn new visual concepts from as few as 2–5 images per class, significantly reducing the data collection burden.
Last reviewed: Jun 11, 2026
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