- A
Image Analysis (object detection) and OCR
Why wrong: Prebuilt object detection in Image Analysis can detect common objects but is not trained for specific product defects like scratches or dents.
- B
Custom Vision (object detection) and OCR
Custom Vision object detection can be trained to identify and locate defects, while OCR reads the serial numbers. This combination solves both tasks effectively.
- C
Face API and OCR
Why wrong: Face API is designed for human faces, not product defects. OCR could read text but the defect detection requirement is unaddressed.
- D
Image Analysis (tags) and OCR
Why wrong: Image Analysis tags provide labels for the entire image without localization. Defect detection requires pinpointing where the defect is, which tags cannot do.
Quick Answer
The correct answer is combining Custom Vision object detection with OCR. This pairing is necessary because the scenario demands two distinct capabilities: identifying visual defects like scratches or dents requires a trained object detection model, while reading serial numbers in various fonts requires optical character recognition. Custom Vision allows you to train a model on labeled images of specific defects, whereas Azure’s OCR (via the Computer Vision Read API) extracts printed text regardless of font or style. On the AI-900 exam, this question tests your understanding of when to use pre-built Computer Vision features versus Custom Vision’s trainable models—a common trap is assuming one feature handles both tasks. Remember the memory tip: “Detect the defect, read the text” to keep the two functions separate.
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 manufacturing company uses Azure Computer Vision to analyze assembly line images. They need to identify specific product defects (e.g., scratches, dents) and also read serial numbers printed on the products in various fonts. Which combination of Azure Computer Vision features should they use?
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) and OCR
Option B is correct because the scenario requires two distinct capabilities: identifying specific defect types (scratches, dents) and reading variable-font serial numbers. Custom Vision's object detection model can be trained on labeled defect images to recognize those specific patterns, while Azure's OCR (part of Computer Vision's Read API) extracts printed text regardless of font. Combining these two features directly addresses both requirements.
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 Analysis (object detection) and OCR
Why it's wrong here
Prebuilt object detection in Image Analysis can detect common objects but is not trained for specific product defects like scratches or dents.
- ✓
Custom Vision (object detection) and OCR
Why this is correct
Custom Vision object detection can be trained to identify and locate defects, while OCR reads the serial numbers. This combination solves both tasks effectively.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Face API and OCR
Why it's wrong here
Face API is designed for human faces, not product defects. OCR could read text but the defect detection requirement is unaddressed.
- ✗
Image Analysis (tags) and OCR
Why it's wrong here
Image Analysis tags provide labels for the entire image without localization. Defect detection requires pinpointing where the defect is, which tags cannot do.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume the built-in Image Analysis object detection can be customized for defects, but it is a pre-trained general model, whereas Custom Vision is required for custom training.
Detailed technical explanation
How to think about this question
Custom Vision object detection uses transfer learning on a ResNet-based architecture, allowing fine-tuning with as few as 50 images per defect class to achieve high accuracy. The OCR capability in Azure Computer Vision (Read API) employs a multi-stage pipeline: text detection via a CRAFT-like model, followed by recognition using a CRNN with attention, which handles varying fonts and orientations robustly. In a real-world assembly line, these two services can be orchestrated via a single API call to Custom Vision for defect bounding boxes, then cropping those regions for OCR to read serial numbers only on non-defective products.
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
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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) and OCR — Option B is correct because the scenario requires two distinct capabilities: identifying specific defect types (scratches, dents) and reading variable-font serial numbers. Custom Vision's object detection model can be trained on labeled defect images to recognize those specific patterns, while Azure's OCR (part of Computer Vision's Read API) extracts printed text regardless of font. Combining these two features directly addresses both requirements.
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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Last reviewed: Jun 11, 2026
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