The answer is Custom Vision – Image classification. This is the correct choice because the scenario requires training a model to assign a single label—defective or acceptable—to each bottle image based on its overall appearance, which is the core function of image classification. Unlike object detection, which locates multiple defects within an image, image classification evaluates the entire image to predict one category. On the Microsoft Azure AI Fundamentals AI-900 exam, this question tests your ability to distinguish between Custom Vision’s image classification and object detection services, a common trap where learners confuse “finding defects” with “classifying the whole item.” Remember: if the task is to decide whether a bottle is good or bad based on its full appearance, you need image classification. A useful memory tip is “Classify the bottle, don’t box the crack”—classification labels the whole image, while detection draws boxes around specific flaws.
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 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?
Why wrong: Object detection identifies and locates objects within an image, which is more complex than needed; the requirement is only to classify the entire bottle.
B
Custom Vision – Image classification
Image classification assigns a label to the entire image, perfectly matching the need to classify bottles as defective or acceptable.
C
Optical Character Recognition (OCR)
Why wrong: OCR extracts text from images, not relevant for classifying bottle defects.
D
Face API
Why wrong: Face API is designed for detecting and analyzing human faces, not bottle inspection.
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
Option B is correct because 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.
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.
✗
Custom Vision – Object detection
Why it's wrong here
Object detection identifies and locates objects within an image, which is more complex than needed; the requirement is only to classify the entire bottle.
✓
Custom Vision – Image classification
Why this is correct
Image classification assigns a label to the entire image, perfectly matching the need to classify bottles as defective or acceptable.
Related concept
Read the scenario before looking for a memorised answer.
✗
Optical Character Recognition (OCR)
Why it's wrong here
OCR extracts text from images, not relevant for classifying bottle defects.
✗
Face API
Why it's wrong here
Face API is designed for detecting and analyzing human faces, not bottle inspection.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that 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.
Detailed technical explanation
How to think about this question
Under the hood, Custom Vision – Image classification uses transfer learning with a convolutional neural network (CNN) pre-trained on ImageNet, fine-tuned on the user's labeled dataset. The model outputs a probability distribution across classes (e.g., 0.95 defective, 0.05 acceptable), and the threshold for classification can be adjusted via the API. In a real-world scenario, the company might need to handle class imbalance (e.g., far more non-defective bottles) by using weighted loss functions or data augmentation to avoid bias.
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: Custom Vision – Image classification — Option B is correct because 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.
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
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.