- A
Object detection that runs with zero latency for real-time applications
Why wrong: Latency optimisation is performance engineering — zero-shot detection finds objects from text descriptions without class-specific training.
- B
Detecting objects described in text without any training examples of that specific class
Zero-shot detection uses vision-language alignment — finding objects from descriptions rather than class-specific labelled examples.
- C
Detection that works on black and white images (zero colour channels)
Why wrong: Greyscale image detection is a preprocessing consideration — zero-shot means no class-specific training examples required.
- D
An object detection model with zero false positives on the test set
Why wrong: Zero false positives is a performance aspiration — zero-shot is a generalisation capability, not an accuracy descriptor.
Quick Answer
The correct answer is that zero-shot object detection identifies objects in images based solely on a textual description, without requiring any training examples of that specific class. This works because the model uses a joint embedding space where visual features from images are aligned with text features from descriptions, enabling it to generalize to unseen categories at inference time. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your understanding of how Azure Cognitive Services can extend beyond predefined labels, often appearing in scenarios about custom vision without labeled data. A common trap is confusing this with traditional object detection, which requires labeled training images for each class. Remember the key distinction: zero-shot means the model has never seen the object before, yet can detect it from a text prompt alone. Memory tip: think of it as “describe first, detect second”—the text description is the only training the model gets.
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. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
What is 'zero-shot object detection' in computer vision?
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
Detecting objects described in text without any training examples of that specific class
Zero-shot object detection refers to a model's ability to detect objects in images based on a textual description of the target class, without having been trained on any labeled examples of that specific class. This is achieved by leveraging a joint embedding space where visual features and text features are aligned, allowing the model to generalize to unseen categories at inference time.
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.
- ✗
Object detection that runs with zero latency for real-time applications
Why it's wrong here
Latency optimisation is performance engineering — zero-shot detection finds objects from text descriptions without class-specific training.
- ✓
Detecting objects described in text without any training examples of that specific class
Why this is correct
Zero-shot detection uses vision-language alignment — finding objects from descriptions rather than class-specific labelled examples.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Detection that works on black and white images (zero colour channels)
Why it's wrong here
Greyscale image detection is a preprocessing consideration — zero-shot means no class-specific training examples required.
- ✗
An object detection model with zero false positives on the test set
Why it's wrong here
Zero false positives is a performance aspiration — zero-shot is a generalisation capability, not an accuracy descriptor.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is confusing the term 'zero-shot' with performance metrics like latency, image color depth, or accuracy, rather than understanding it as a training paradigm where the model generalizes to unseen classes via natural language descriptions.
Detailed technical explanation
How to think about this question
Under the hood, zero-shot object detection models like OWL-ViT (Open World Localization with Vision Transformers) use a contrastive learning approach, such as CLIP-style embeddings, to map both image regions and text prompts into a shared latent space. At inference, the model computes similarity scores between region proposals and the text query, selecting regions with the highest cosine similarity. A real-world scenario is detecting a rare animal species in camera trap images without having any labeled training images of that species, enabling rapid deployment in conservation monitoring.
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: Detecting objects described in text without any training examples of that specific class — Zero-shot object detection refers to a model's ability to detect objects in images based on a textual description of the target class, without having been trained on any labeled examples of that specific class. This is achieved by leveraging a joint embedding space where visual features and text features are aligned, allowing the model to generalize to unseen categories at inference time.
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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