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 chain uses ceiling-mounted cameras to monitor shelf inventory. They need to identify and locate individual products (e.g., a specific brand of cereal) within an image and count how many are present. Which Azure Computer Vision capability should they use?
Why wrong: Image classifies the entire image as a single category (e.g., 'cereal aisle'), but does not locate or count individual products.
B
Object detection
Object detection identifies and locates multiple objects of interest within an image, providing bounding boxes and enabling counting of each object type.
C
Optical character recognition (OCR)
Why wrong: OCR extracts printed or handwritten text from images; it does not identify or count non-text objects.
D
Semantic segmentation
Why wrong: Semantic segmentation classifies every pixel in the image into categories, which is overkill for simply locating and counting products and typically used for more detailed analysis.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Object detection
Object detection is the correct capability because it not only identifies the presence of a specific product (e.g., a brand of cereal) within an image but also localizes each instance by drawing bounding boxes around them, enabling an accurate count. Image classification would only label the entire image as containing cereal without locating individual boxes, while OCR and semantic segmentation serve different purposes (text extraction and pixel-level labeling, respectively).
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.
✗
Image classification
Why it's wrong here
Image classifies the entire image as a single category (e.g., 'cereal aisle'), but does not locate or count individual products.
✓
Object detection
Why this is correct
Object detection identifies and locates multiple objects of interest within an image, providing bounding boxes and enabling counting of each object type.
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; it does not identify or count non-text objects.
✗
Semantic segmentation
Why it's wrong here
Semantic segmentation classifies every pixel in the image into categories, which is overkill for simply locating and counting products and typically used for more detailed analysis.
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 labeling the image as 'cereal' is sufficient to count items, when in fact object detection is required for instance-level localization and counting.
Detailed technical explanation
How to think about this question
Under the hood, Azure's object detection uses deep learning models (e.g., Faster R-CNN or YOLO) that output bounding box coordinates and class probabilities for each detected object. A subtle behavior is that the model's confidence threshold can be tuned to reduce false positives (e.g., mistaking a similar-shaped box for the target cereal), and non-maximum suppression (NMS) is applied to eliminate duplicate detections of the same object. In a real-world retail scenario, object detection can also track inventory across multiple camera feeds by associating detected boxes over time.
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: Object detection — Object detection is the correct capability because it not only identifies the presence of a specific product (e.g., a brand of cereal) within an image but also localizes each instance by drawing bounding boxes around them, enabling an accurate count. Image classification would only label the entire image as containing cereal without locating individual boxes, while OCR and semantic segmentation serve different purposes (text extraction and pixel-level labeling, respectively).
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.