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 transportation company wants to automatically identify whether an image contains a car, a truck, or a motorcycle. The system should output a single label for the entire image. Which computer vision capability in Azure should they use?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Image classification
Image classification assigns a single label to an entire image based on its dominant content. Since the requirement is to output one label (car, truck, or motorcycle) per image, this maps directly to Azure's Custom Vision image classification capability, which trains a model to categorize whole images into predefined classes.
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.
✗
Object detection
Why it's wrong here
Object detection locates objects with bounding boxes and provides multiple labels per image, not a single overall label.
✓
Image classification
Why this is correct
Image classification assigns one or more labels to the entire image, matching the requirement to identify the type of vehicle shown.
Related concept
Read the scenario before looking for a memorised answer.
✗
Optical Character Recognition (OCR)
Why it's wrong here
OCR extracts printed or handwritten text from images and is not used for recognizing vehicle types.
✗
Semantic segmentation
Why it's wrong here
Semantic segmentation classifies each individual pixel in an image, which is more detailed than simply labeling the entire image.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse object detection (which finds and labels multiple objects) with image classification (which labels the whole image), especially when the question mentions multiple vehicle types, leading them to incorrectly choose object detection.
Detailed technical explanation
How to think about this question
Azure Custom Vision image classification uses convolutional neural networks (CNNs) to learn hierarchical features from training images. The model outputs a probability distribution across classes via a softmax layer, and the highest-probability class becomes the single label. In a real-world scenario, if an image contains both a car and a truck, image classification would still output only one label (e.g., 'car') based on the dominant visual features, which is why object detection would be needed if multiple objects must be identified simultaneously.
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: Image classification — Image classification assigns a single label to an entire image based on its dominant content. Since the requirement is to output one label (car, truck, or motorcycle) per image, this maps directly to Azure's Custom Vision image classification capability, which trains a model to categorize whole images into predefined classes.
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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