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

AI-900 Practice Question: Describe features of computer vision workloads on Azure

A manufacturing company wants to use Azure Computer Vision to inspect products on an assembly line for defects. They have a labeled dataset with images of defective and non-defective products. They need to not only classify products as defective or not, but also identify the exact location of the defect (e.g., a crack) in the image. Which Azure Computer Vision capability should they use?

⚠ Common exam trap

It's easy for candidates to confuse image classification (which only labels the whole image) with object detection (which provides both classification and localization), leading them to choose Custom Vision image classification despite the explicit need for defect location.

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 not only classifies images (defective vs. non-defective) but also localizes defects by drawing bounding boxes around them. The labeled dataset with defect locations directly supports training a model to output both class labels and spatial coordinates, which is exactly what object detection provides.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✓
Custom Vision object detection
Why this is correct
Custom Vision object detection is the correct service because it can be trained on labeled images of defective parts to identify and localize defects such as cracks or scratches. The model outputs bounding boxes around each detected defect, along with a class label and confidence score, so the company knows both what the defect is and where it occurs. Unlike image classification, it can detect multiple defects within a single image and support downstream actions like automated rejection or repair.
✗
Custom Vision image classification
Why it's wrong here
Custom Vision image classification assigns a single label to the entire image, such as 'defective' or 'acceptable', but it does not provide any location information about where in the image the defect appears. This is insufficient for manufacturing quality control because operators or automated systems need the defect's position to guide rework, scrap decisions, or downstream processing. It also cannot count multiple defects or distinguish between different defect types occurring in the same image.
When this WOULD be correct
If the question asked: 'A manufacturing company wants to classify product images as either defective or non-defective, without needing to locate the defect. Which Azure Computer Vision capability should they use?' then Custom Vision image classification would be correct.
✗
Azure Face API
Why it's wrong here
Azure Face API is a specialized service designed for detecting and analyzing human faces, including facial landmarks, emotions, age, and identity. It is not a general-purpose visual anomaly detector, and its models are trained exclusively on face data, so it will not recognize cracks, corrosion, or other industrial defects. In a manufacturing scenario, face detection would produce no useful output on product images and would fail to meet the requirement. It is a valid Azure Computer Vision resource, but wrong for this use case.
When this WOULD be correct
An exam scenario where Azure Face API would be correct: 'A security company wants to identify and locate faces in surveillance images to detect unauthorized individuals in a restricted area.'
✗
Optical Character Recognition (OCR)
Why it's wrong here
Optical Character Recognition (OCR) is a pre-built Azure AI service that extracts printed or handwritten text from images, returning text content and its bounding box coordinates. It has no understanding of manufacturing defects, surface anomalies, or non-text visual patterns, so it cannot detect cracks, dents, or contamination. Running OCR on product images would simply return empty or irrelevant text results and provide no value for defect inspection.
When this WOULD be correct
A company needs to extract printed serial numbers or expiration dates from product labels on an assembly line to track inventory. In that case, OCR would be the correct choice.

Option-by-option analysis

Why each answer is right or wrong

Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The AI-900 exam frequently reuses these exact scenarios with slightly different constraints.

✓Custom Vision object detectionCorrect answer▾

Why this is correct

Custom Vision object detection is the correct service because it can be trained on labeled images of defective parts to identify and localize defects such as cracks or scratches. The model outputs bounding boxes around each detected defect, along with a class label and confidence score, so the company knows both what the defect is and where it occurs. Unlike image classification, it can detect multiple defects within a single image and support downstream actions like automated rejection or repair.

✗Custom Vision image classificationWrong answer — click to see why▾

Why this is wrong here

Image classification only assigns a single label to the entire image (e.g., 'defective' or 'non-defective'), but does not provide the location of the defect. The question explicitly requires identifying the exact location of the defect, which demands object detection.

★ When this WOULD be the correct answer

If the question asked: 'A manufacturing company wants to classify product images as either defective or non-defective, without needing to locate the defect. Which Azure Computer Vision capability should they use?' then Custom Vision image classification would be correct.

Why candidates choose this

Candidates may confuse image classification with object detection, assuming that classification can also identify where defects are, or they may not fully understand the difference between these two capabilities.

✗Azure Face APIWrong answer — click to see why▾

Why this is wrong here

Azure Face API is designed for detecting and analyzing human faces, not for identifying defects in manufactured products. The question requires locating defects in product images, which is unrelated to facial analysis.

★ When this WOULD be the correct answer

An exam scenario where Azure Face API would be correct: 'A security company wants to identify and locate faces in surveillance images to detect unauthorized individuals in a restricted area.'

Why candidates choose this

Candidates may confuse 'detection' in Face API with general object detection, not realizing Face API is specialized for human faces only.

✗Optical Character Recognition (OCR)Wrong answer — click to see why▾

Why this is wrong here

OCR is designed to extract text from images, not to detect or localize defects like cracks in products. The question requires identifying the exact location of a defect, which OCR cannot do.

★ When this WOULD be the correct answer

A company needs to extract printed serial numbers or expiration dates from product labels on an assembly line to track inventory. In that case, OCR would be the correct choice.

Why candidates choose this

Candidates may think OCR can 'read' defects as patterns, or they confuse OCR with general image analysis, not realizing it is limited to text extraction.

Analysis generated from the official AI-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”

Go deeper

AI-900 Practice Question: Describe features of computer vision workloads on Azure

Why each option matters

Option-by-option breakdown

Why each answer is right or wrong

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