- A
Use the pre-built Computer Vision object detection API directly.
Why wrong: The pre-built object detection model can detect common objects like cars, people, and animals, but it is not trained to recognize penguins. Its accuracy would be very low for this specific use case.
- B
Train a Custom Vision object detection model using the labeled images.
Custom Vision enables training a specialized object detection model with a small set of labeled images. With only one object class ('penguin'), 500 images are more than sufficient to achieve good accuracy for detection and counting.
- C
Use the Computer Vision Image Analysis API with the 'dense captioning' feature.
Why wrong: Dense captioning generates descriptions for regions of an image, but it is not designed for precise object detection or counting. It also relies on pre-trained models that do not include penguins.
- D
Train a Custom Vision image classification model with the labeled images.
Why wrong: Image classification only tells whether a penguin is present in the image, not where it is or how many there are. For counting exact individuals, object detection or instance segmentation is required.
Quick Answer
The answer is to train a Custom Vision object detection model using the labeled images. This is correct because pre-built Computer Vision APIs are optimized for common, well-defined objects and often fail at custom vision object detection for small camouflaged objects like penguins blending into rocky terrain. Custom Vision allows you to train a model on your own 500 labeled drone images, teaching it to recognize the specific patterns, partial occlusions, and low-contrast features unique to this challenging environment. On the AI-900 exam, this scenario tests your understanding of when to use Custom Vision versus pre-built services—a common trap is assuming the general object detection API works for all cases. Remember: if the objects are unusual, small, or camouflaged, you need to train your own model. Memory tip: “Custom for camouflaged, pre-built for common.”
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 wildlife research team uses drone imagery to monitor penguin populations in a remote area. The penguins are small, blend into the rocky background, and are often only partially visible. The team has a limited set of 500 labeled drone images showing penguins. They want to build a system that accurately detects and counts penguins. Which approach should they take using Azure AI services?
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
Train a Custom Vision object detection model using the labeled images.
The pre-built Computer Vision object detection API is optimized for common objects and may not perform well on small, camouflaged penguins in rocky terrain. Custom Vision allows the team to train a dedicated object detection model using their 500 labeled images, enabling the model to learn the specific visual features of penguins in this challenging environment. This approach is ideal for domain-specific detection tasks where off-the-shelf models lack accuracy.
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.
- ✗
Use the pre-built Computer Vision object detection API directly.
Why it's wrong here
The pre-built object detection model can detect common objects like cars, people, and animals, but it is not trained to recognize penguins. Its accuracy would be very low for this specific use case.
- ✓
Train a Custom Vision object detection model using the labeled images.
Why this is correct
Custom Vision enables training a specialized object detection model with a small set of labeled images. With only one object class ('penguin'), 500 images are more than sufficient to achieve good accuracy for detection and counting.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use the Computer Vision Image Analysis API with the 'dense captioning' feature.
Why it's wrong here
Dense captioning generates descriptions for regions of an image, but it is not designed for precise object detection or counting. It also relies on pre-trained models that do not include penguins.
- ✗
Train a Custom Vision image classification model with the labeled images.
Why it's wrong here
Image classification only tells whether a penguin is present in the image, not where it is or how many there are. For counting exact individuals, object detection or instance segmentation is required.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse image classification with object detection, assuming a single label per image can solve a counting problem, or overestimate the generic API's ability to handle niche, low-contrast objects without custom training.
Detailed technical explanation
How to think about this question
Custom Vision object detection uses transfer learning from a base model (e.g., ResNet) and fine-tunes it on the user's labeled dataset, requiring as few as 50 images per class but benefiting from hundreds for complex scenarios. The model outputs bounding boxes and confidence scores, which can be thresholded to count penguins even when partially occluded. Under the hood, it employs region proposal networks (RPNs) to identify candidate object locations, making it robust to small object sizes and background clutter.
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: Train a Custom Vision object detection model using the labeled images. — The pre-built Computer Vision object detection API is optimized for common objects and may not perform well on small, camouflaged penguins in rocky terrain. Custom Vision allows the team to train a dedicated object detection model using their 500 labeled images, enabling the model to learn the specific visual features of penguins in this challenging environment. This approach is ideal for domain-specific detection tasks where off-the-shelf models lack accuracy.
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
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.
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