Question 261 of 1,020

Describe features of computer vision workloads on Azure →mediumMultiple ChoiceObjective-mapped

Quick Answer

The answer is Custom Vision object detection. This service is correct because it allows you to train a custom model on your labeled images to detect and localize specific product categories like 'soft drinks' or 'chips' using bounding boxes, and then count the number of items per category—directly supporting the requirement for inventory counting and shelf state detection. On the AI-900 exam, this question tests your ability to distinguish between pre-built Computer Vision features (which offer general detection but cannot be retrained for custom categories) and Custom Vision, which is designed for bespoke object detection tasks. A common trap is choosing the standard Object Detection API, but remember: if the scenario mentions training on your own labeled images to identify specific items, Custom Vision is the only service that enables that custom model building. Memory tip: "Custom" means you bring your own labeled data to train a unique 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 retail company uses overhead cameras to monitor shelf inventory in a store. They want to build a system that automatically detects whether a shelf section is empty or stocked, and specifically identify product categories (e.g., 'soft drinks', 'chips', 'canned goods') and count the number of items in each category. The company has a large set of labeled images showing different shelf states. Which Azure Computer Vision service should they use to build this custom detection and counting solution?

Question 1mediummultiple choice

Full question →

A
Computer Vision Image Analysis with dense captioning
Why wrong: Dense captioning generates descriptive captions for regions of an image, but it does not provide the precise object detection and counting capabilities needed for inventory management. It is not designed for training on custom categories.
B
Custom Vision object detection
Custom Vision object detection is specifically designed for training models to detect and locate objects of interest. With labeled images of product categories, you can create a model that outputs bounding boxes around each detected item, enabling counting.
C
Optical Character Recognition (OCR)
Why wrong: OCR is used to extract printed or handwritten text from images. Shelf items may have text, but the core requirement is to detect and count product categories, not to read text. OCR alone cannot identify product categories visually.
D
Azure Machine Learning with a pre-trained YOLO model
Why wrong: While technically possible to use a pre-trained model via Azure Machine Learning, the question asks for an Azure Computer Vision service. Custom Vision is a dedicated PaaS offering that abstracts the complexity and is the most straightforward Azure service for this task.

Full breakdown with real-world context →

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

✓

Custom Vision object detection

Custom Vision object detection is the correct choice because it allows the company to train a model on their labeled images to detect and localize specific product categories (e.g., 'soft drinks', 'chips') and count items within each category. Unlike pre-built Computer Vision features, Custom Vision enables custom object detection with bounding boxes and classification, which directly supports the requirement for detecting shelf states and counting items per category.

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.

✗
Computer Vision Image Analysis with dense captioning
Why it's wrong here
Dense captioning generates descriptive captions for regions of an image, but it does not provide the precise object detection and counting capabilities needed for inventory management. It is not designed for training on custom categories.
✓
Custom Vision object detection
Why this is correct
Custom Vision object detection is specifically designed for training models to detect and locate objects of interest. With labeled images of product categories, you can create a model that outputs bounding boxes around each detected item, enabling counting.
Related concept
Read the scenario before looking for a memorised answer.
✗
Optical Character Recognition (OCR)
Why it's wrong here
OCR is used to extract printed or handwritten text from images. Shelf items may have text, but the core requirement is to detect and count product categories, not to read text. OCR alone cannot identify product categories visually.
✗
Azure Machine Learning with a pre-trained YOLO model
Why it's wrong here
While technically possible to use a pre-trained model via Azure Machine Learning, the question asks for an Azure Computer Vision service. Custom Vision is a dedicated PaaS offering that abstracts the complexity and is the most straightforward Azure service for this task.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates confuse pre-built Computer Vision features (like dense captioning or OCR) with Custom Vision, assuming any Azure Computer Vision service can be customized without training, but only Custom Vision supports custom object detection with bounding boxes and counting.

Detailed technical explanation

How to think about this question

Custom Vision object detection uses transfer learning on a base model (e.g., ResNet) to fine-tune on custom labeled images, outputting bounding boxes and class probabilities for each detected object. The service supports counting by iterating over detected objects per class, and its performance can be optimized by providing at least 15-50 images per class with varied angles and lighting. In a real-world scenario, the company would need to ensure labeled images include both empty and stocked states to avoid false positives from empty shelves being misclassified as a product category.

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.

What to study next

Got this wrong? Here's your next step.

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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FAQ

Questions learners often ask

What does this AI-900 question test?

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 object detection — Custom Vision object detection is the correct choice because it allows the company to train a model on their labeled images to detect and localize specific product categories (e.g., 'soft drinks', 'chips') and count items within each category. Unlike pre-built Computer Vision features, Custom Vision enables custom object detection with bounding boxes and classification, which directly supports the requirement for detecting shelf states and counting items per category.

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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