Question 1mediummultiple choice
Full question →mediummultiple choiceObjective-mapped
A retail company wants to use Azure Computer Vision to monitor shelf inventory. They need to detect whether specific products (e.g., 'Brand A cereal', 'Brand B cereal') are present on a shelf and count the number of units of each product. They have a labeled dataset with images of each product category. Which Azure Computer Vision capability should they use?
Answer choices
Why each option matters
Good practice is not just finding the correct option. The wrong answers often show the exact trap the exam wants you to fall into.
A
Best answer
Custom object detection
With custom object detection, you can train a model on labeled images to detect specific products (e.g., Brand A cereal) and count their occurrences, meeting the requirement.
B
Distractor review
Optical character recognition (OCR)
OCR reads text from images (e.g., product labels), but it does not identify or count the physical product objects themselves.
C
Distractor review
Prebuilt image analysis (Describe Image)
Prebuilt image analysis generates tags or captions for common objects, but it cannot be customized to detect and count specific branded products reliably.
D
Distractor review
Facial recognition
Facial recognition identifies human faces; it is not suitable for detecting retail products on shelves.
Common exam trap
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Technical deep dive
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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.
Related practice questions
Related AI-900 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
More questions from this exam
Keep practising from the same exam bank, or move into a focused topic page if this question exposed a weak area.
Question 1
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Question 2
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Question 3
A developer is using Azure OpenAI Service to generate product descriptions from technical specifications. The generated descriptions sometimes include plausible-sounding but incorrect details (hallucinations). The developer wants to ensure the model's responses are strictly based on the provided product data and does not add any external or invented information. Which approach should the developer use?
Question 4
A developer is using Azure OpenAI with GPT-4 to build a chatbot that answers legal questions based on a company's internal policy documents. The developer wants the model's responses to be maximally deterministic and factual, avoiding any creative or speculative language. Which parameter should the developer set to the lowest possible value in the API call?
Question 5
A developer is using Azure OpenAI to generate creative product descriptions. The outputs are often repetitive and lack variety. The developer wants to increase the diversity of the generated text while still keeping it coherent. Which parameter should the developer increase?
Question 6
A developer is using Azure OpenAI Service to generate product descriptions. They want the output to be highly focused and deterministic, with less randomness. Which parameter should they decrease?
FAQ
Questions learners often ask
What does this AI-900 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Custom object detection — Custom object detection (via Custom Vision or Azure AI Custom Vision) allows training a model to detect and count instances of specific objects (e.g., product types) in images. Optical character recognition (OCR) extracts text, not product instances. Image analysis prebuilt tags provide general labels, not custom product counting. Facial recognition is for human faces.
What should I do if I get this AI-900 question wrong?
Then try more questions from the same exam bank and focus on understanding why the wrong options are tempting.
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