The answer is Object Detection. This Azure Computer Vision capability is the correct choice because it not only identifies whether a specific part is present in an image but also returns bounding box coordinates that mark the exact location of each detected object, which directly meets the requirement to confirm the part’s presence and draw a box around it. On the Microsoft Azure AI Fundamentals AI-900 exam, this question tests your ability to distinguish between Computer Vision services like Image Classification (which only labels the whole image) and Object Detection (which locates objects spatially). A common trap is confusing Object Detection with Semantic Segmentation, but remember that segmentation draws pixel-level outlines, not simple boxes. For manufacturing quality control scenarios using overhead cameras, Object Detection is the go-to capability. A useful memory tip: think “Detect and Box” — if you need to both find an object and draw a rectangle around it, Object Detection is your answer.
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. Examine the command output carefully: the correct answer depends on what the output actually shows, not on general recall alone. 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 manufacturing company uses overhead cameras on an assembly line to check that each part is present in the correct location on a circuit board. The system must not only confirm the part is there but also draw a box around each part to show its exact position. Which Azure Computer Vision capability should they use?
Why wrong: OCR is designed to extract printed or handwritten text from images. It cannot locate non-text objects like circuit board parts.
B
Image Classification
Why wrong: Image Classification assigns a single label (e.g., 'part present' or 'part missing') to the whole image. It does not provide bounding boxes to show where objects are located.
C
Object Detection
Object Detection identifies objects and returns their bounding box coordinates, making it suitable for locating each part and drawing boxes around them.
D
Face Detection
Why wrong: Face Detection is specialized for detecting human faces. It is not designed to locate inanimate objects like circuit board parts.
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 whether a specific object (like a circuit board part) is present in an image but also returns bounding box coordinates that indicate the exact location of each detected object. This meets the requirement to both confirm the part's presence and draw a box around it.
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.
✗
Optical Character Recognition (OCR)
Why it's wrong here
OCR is designed to extract printed or handwritten text from images. It cannot locate non-text objects like circuit board parts.
✗
Image Classification
Why it's wrong here
Image Classification assigns a single label (e.g., 'part present' or 'part missing') to the whole image. It does not provide bounding boxes to show where objects are located.
✓
Object Detection
Why this is correct
Object Detection identifies objects and returns their bounding box coordinates, making it suitable for locating each part and drawing boxes around them.
Related concept
Read the scenario before looking for a memorised answer.
✗
Face Detection
Why it's wrong here
Face Detection is specialized for detecting human faces. It is not designed to locate inanimate objects like circuit board parts.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse Image Classification (which only labels the whole image) with Object Detection (which provides per-object localization), especially when the question emphasizes both 'confirm the part is there' and 'draw a box around each part'.
Trap categories for this question
Command / output trap
Image Classification assigns a single label (e.g., 'part present' or 'part missing') to the whole image. It does not provide bounding boxes to show where objects are located.
Detailed technical explanation
How to think about this question
Azure's Object Detection API uses deep learning models like YOLO (You Only Look Once) or Faster R-CNN to output a list of detected objects, each with a confidence score and a bounding box defined by x, y, width, and height coordinates. In a real-world assembly line, the system can be trained on a custom dataset of circuit board parts using Azure Custom Vision to achieve high precision, and the bounding boxes enable downstream processes like pick-and-place verification or defect logging.
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 whether a specific object (like a circuit board part) is present in an image but also returns bounding box coordinates that indicate the exact location of each detected object. This meets the requirement to both confirm the part's presence and draw a box around it.
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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