AI-901 Practice Questions: The 5 Hardest Scenarios You Will Face (2026)
If you are preparing for the new Microsoft AI-901 Azure AI Fundamentals certification, you have probably noticed a major shift. When Microsoft retired the legacy AI-900, they changed the testing format. It is no longer just a vocabulary test where you memorize basic definitions. The new AI-901 exam requires you to read enterprise scenarios and choose the correct Azure AI service to solve the business problem.
To help you gauge your readiness, we pulled 5 of the hardest scenario questions from our 155-question AI-901 exam simulator. Try to answer them without looking at the explanations below.
Question 1: The Foundry Governance Scenario
Domain: Generative AI and Microsoft Foundry
Axio Technologies is deploying a generative AI assistant using Microsoft Foundry. Different business units need to build their own AI applications but the security team requires that all deployments across the organisation share the same content safety policies automatically without each team configuring them individually. What is the correct approach in Microsoft Foundry?
- A) Configure content safety policies in each individual application's system prompt
- B) Set content safety policies at the hub level so they apply across all projects and deployments automatically
- C) Create a separate Azure OpenAI resource for each business unit with individual policies
- D) Use Azure Policy to enforce content safety rules at the subscription level
Reveal the answer and explanation
Correct answer: B
Hub-level configuration in Microsoft Foundry applies governance policies across all projects and deployments within that hub automatically. This is the correct architectural pattern for organisation-wide governance: configure once at the hub level and every project and deployment beneath it inherits the policy without manual configuration.
Option A is the least reliable approach, because system prompt instructions can be overridden by user input and must be maintained separately in every individual application, creating gaps.
Option C would require each team to manage their own Azure OpenAI resource and policies independently, increasing administrative overhead and risk of inconsistency.
Option D uses Azure Policy, which operates at the Azure resource level for infrastructure governance. It does not control content safety behaviour within Microsoft Foundry AI deployments.
Question 2: Choosing the Right Vision Model
Domain: Computer Vision
A manufacturing company needs an AI solution to scan images of hardware on an assembly line. The system must count the number of screws on a tray and send an alert if a tray is missing a screw. You need to recommend the appropriate Azure AI service.
- A) Azure AI Face
- B) Azure OpenAI (DALL-E 3)
- C) Azure AI Vision (Spatial Analysis)
- D) Custom Vision (Object Detection)
Reveal the answer and explanation
Correct answer: D
This is a classic use case for Object Detection. Azure AI Custom Vision allows you to train a model with your own specific images (in this case, screws on a tray) to detect the location and quantity of specific objects within an image. Azure AI Vision is for pre-trained generic image analysis, and DALL-E is for generating images, not analyzing them.
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Question 3: The Content Safety Trap
Domain: Responsible AI
You are building an AI chatbot for a mental health clinic using Azure OpenAI. You need to ensure the chatbot automatically blocks and flags user prompts that contain self-harm or violent language before the prompt ever reaches the GPT model. Which specific feature should you configure?
- A) Azure OpenAI Content Filters
- B) Azure Key Vault
- C) Microsoft Purview Information Protection
- D) Azure AI Personalizer
Reveal the answer and explanation
Correct answer: A
Azure OpenAI includes built-in Content Filters (powered by Azure AI Content Safety). These filters run synchronously with your API calls, evaluating both the user's input prompt and the model's output for categories like hate, sexual, violence, and self-harm. Purview is for data governance, not real-time prompt filtering.
Question 4: Conversational AI Architecture
Domain: Natural Language Processing (NLP)
A retail bank wants to build a voice-enabled virtual assistant. Customers will call a phone number, speak their account number, and ask for their balance. The system needs to convert the customer's spoken words into text, and then determine the customer's intent (for example, "Check Balance"). Which two Azure AI services must be combined to achieve this?
- A) Azure AI Speech and Azure AI Language (CLU)
- B) Azure AI Document Intelligence and Azure OpenAI
- C) Azure AI Translator and Azure AI Search
- D) Azure AI Speech and Azure AI Immersive Reader
Reveal the answer and explanation
Correct answer: A
You need two distinct services here. First, Azure AI Speech (specifically Speech-to-Text) is required to transcribe the caller's audio into text. Second, Azure AI Language (specifically Conversational Language Understanding or CLU) is used to parse that text and extract the "intent" (Check Balance) and "entities" (Account Number) so your backend application can process the request.
Question 5: The Responsible AI Transparency Scenario
Domain: Responsible AI
A retail bank is using an Azure AI Language model to automatically approve or reject customer loan applications. A customer calls the bank to complain that their application was rejected but cannot get any explanation of why the decision was made. Which responsible AI principle has not been adequately addressed in this system?
- A) Fairness
- B) Reliability and safety
- C) Transparency
- D) Inclusiveness
Reveal the answer and explanation
Correct answer: C
Transparency requires that AI systems and their decisions are understandable and explainable to the people they affect. A customer who receives an automated decision but cannot get any explanation of the reasoning behind it is experiencing a transparency failure. The principle requires that individuals be able to understand how AI systems work and why they produce the outcomes they do.
Option A (fairness) would be the primary concern if there were evidence the system was systematically disadvantaging people based on demographic characteristics such as ethnicity or gender. The scenario does not describe this.
Option B (reliability and safety) relates to the system performing consistently and not causing unintended harm. There is no indication in the scenario that the system is performing inconsistently.
Option D (inclusiveness) relates to designing AI that works equitably for all people regardless of ability, background, or circumstance. The scenario is specifically about explainability of a decision, not about inclusive access.
How did you score? If you struggled with the architectural reasoning behind these questions, do not sit the actual AI-901 exam yet. The new format requires you to understand when to use which service, not just what they are called.
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