- A
A product listing of Azure AI hardware accelerators available for purchase
Why wrong: Hardware purchasing is infrastructure procurement — the model catalogue is a software library of AI models for deployment.
- B
A curated collection of AI models from multiple providers available for deployment in Azure
The model catalogue hosts OpenAI, open-source, and Microsoft models — enabling discovery and deployment of the right model for each use case.
- C
A directory of all Azure AI customer support contacts organised by model type
Why wrong: Customer support contacts are support resources — the model catalogue is a technical library of deployable AI models.
- D
A registry of all models that have passed Microsoft's responsible AI certification
Why wrong: While responsible AI evaluation is part of the platform, the catalogue is broadly a model discovery and deployment hub — not exclusively a certification registry.
AI-900 Practice Question: Describe features of generative AI workloads on Azure
This AI-900 practice question tests your understanding of describe features of generative ai 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.
What is the 'model catalogue' in Azure AI Foundry/AI Studio?
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
A curated collection of AI models from multiple providers available for deployment in Azure
The model catalogue in Azure AI Foundry (formerly AI Studio) is a curated collection of AI models from multiple providers, including OpenAI, Meta, Hugging Face, and Microsoft, that can be deployed and fine-tuned directly within the Azure environment. It simplifies the process of discovering, comparing, and deploying foundation models for generative AI workloads without requiring manual setup or external registries.
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.
- ✗
A product listing of Azure AI hardware accelerators available for purchase
Why it's wrong here
Hardware purchasing is infrastructure procurement — the model catalogue is a software library of AI models for deployment.
- ✓
A curated collection of AI models from multiple providers available for deployment in Azure
Why this is correct
The model catalogue hosts OpenAI, open-source, and Microsoft models — enabling discovery and deployment of the right model for each use case.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
A directory of all Azure AI customer support contacts organised by model type
Why it's wrong here
Customer support contacts are support resources — the model catalogue is a technical library of deployable AI models.
- ✗
A registry of all models that have passed Microsoft's responsible AI certification
Why it's wrong here
While responsible AI evaluation is part of the platform, the catalogue is broadly a model discovery and deployment hub — not exclusively a certification registry.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse the model catalogue with a hardware listing or a certification registry, because Azure AI Foundry's interface includes both compute options and responsible AI dashboards, leading test-takers to incorrectly associate the catalogue with those unrelated features.
Detailed technical explanation
How to think about this question
Under the hood, the model catalogue leverages Azure Machine Learning's model registry and integrates with the Azure AI Inference API to provide a unified endpoint for deploying models from providers like OpenAI, Meta (Llama), and Mistral. Each model in the catalogue includes a model ID, version, and associated deployment SKU (e.g., Standard, Pay-as-you-go), and can be deployed as a serverless API endpoint with managed throughput. A real-world scenario is a developer using the catalogue to deploy a Llama 2 model for a chatbot, then switching to GPT-4 for summarization—all without leaving the Azure portal.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this AI-900 question test?
Describe features of generative AI workloads on Azure — This question tests Describe features of generative AI workloads on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: A curated collection of AI models from multiple providers available for deployment in Azure — The model catalogue in Azure AI Foundry (formerly AI Studio) is a curated collection of AI models from multiple providers, including OpenAI, Meta, Hugging Face, and Microsoft, that can be deployed and fine-tuned directly within the Azure environment. It simplifies the process of discovering, comparing, and deploying foundation models for generative AI workloads without requiring manual setup or external registries.
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.
About these practice questions
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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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