Pass Guaranteed 2026 Microsoft AI-901 Updated Exam Sample

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Microsoft AI-901 Exam Overview:

Certification Vendor:Microsoft
Exam Name:Microsoft Azure AI Fundamentals
Exam Number:AI-900
Passing Score:700 / 1000
Real Exam Qty:40-60
Exam Duration:45-60
Exam Price:USD 99
Certificate Validity Period:1 year (renewable via free online assessment on Microsoft Learn)
Available Languages:French, English, Arabic (Saudi Arabia), German, Indonesian (Indonesia), Russian, Spanish, Portuguese (Brazil), Chinese (Traditional), Japanese, Italian, Korean, Chinese (Simplified)
Exam Format:Multiple select, Multiple choice, Match the service to use case, Scenario-based, Drag and drop
Related Certifications:Microsoft Certified: Azure AI Fundamentals
Sample Questions:Microsoft AI-901 Sample Questions
Exam Way:Online (remote proctored via Pearson VUE) or at a Pearson VUE test center
Pre Condition:No prerequisites. This exam is intended for both technical and non-technical backgrounds. Data science and software engineering experience are not required. Awareness of basic cloud concepts and client-server applications is beneficial.
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/certifications/exams/ai-900

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Microsoft AI-901 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Identify AI concepts and capabilities: This domain covers the foundational knowledge of AI from ethical principles and responsible design to understanding how AI models work and what kinds of tasks they can perform. It also explores the full range of AI workloads including generative AI, computer vision, speech, and information extraction.
Topic 2
  • Implement AI solutions by using Microsoft Foundry: This domain is hands-on and focuses on building and deploying AI solutions using the Microsoft Foundry platform and its associated tools. It spans generative AI apps, text and speech processing, computer vision, and document intelligence all implemented through the Foundry portal and SDK.

Microsoft Azure AI Fundamentals Sample Questions (Q66-Q71):

NEW QUESTION # 66
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 67
You deploy an AI system to assist with hiring decisions.
Company policy requires that human reviewers oversee AI-generated recommendations and remain responsible for final hiring decisions.
Which Microsoft responsible AI principle is this an example of?

Answer: A

Explanation:
Accountability is the Microsoft responsible AI principle that ensures human oversight so that people remain responsible for AI-driven decisions.
Human Oversight: Requiring human review ensures that AI operates under effective governance rather than acting autonomously on high-impact choices.
Final Authority: Keeping humans legally and operationally responsible prevents automated systems from dictating critical outcomes like hiring.
Reference:
https://www.microsoft.com/en-us/ai/principles-and-approach


NEW QUESTION # 68
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation:
The correct answer is API endpoint . After a generative AI model is deployed in Microsoft Foundry, the deployment exposes an inference endpoint through which external applications can submit requests and receive generated responses. Microsoft Foundry documentation describes deployed models as being accessible programmatically through an endpoint and associated credentials. Applications invoke the relevant REST API or SDK against this endpoint and specify the deployed model as required by the API.
An embedding vector is a numerical representation of semantic information used for similarity search, retrieval, and grounding; it is not the network interface through which a deployed model is invoked. A training dataset contains data used to train or fine-tune a model and is not called when performing inference.
A URL parameter can be part of an API request, but it does not represent the deployed model ' s callable interface.
This directly aligns with the AI-901 objective to deploy a model and interact with it in the Foundry portal and to create lightweight applications that invoke deployed generative AI models through supported APIs and SDKs.


NEW QUESTION # 69
Hotspot Question
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation:
Box 1: Named Entity Recognition (NER)
Named Entity Recognition (NER) is used to extract dates, quantities, and locations from text, along with other categories like names of people, organizations, and monetary values.
Reference:
https://www.iamdave.ai/blog/understanding-named-entity-recognition-ner-meaning-methods-and-use-cases/


NEW QUESTION # 70
You are developing a system to predict the prices of insurance for drivers in the United Kingdom. You need to minimize unintended bias in the system. What should you do?

Answer: C

Explanation:
To minimize unintended bias, the training data should be representative of the population for which the model will be used. Microsoft responsible AI guidance identifies fairness as a key principle and notes that discrepancies can occur when systems underperform for specific demographic groups or rarely observed input conditions in training data.
A global insurance dataset may not represent UK drivers. A completely random sample may still underrepresent important groups. Removing protected characteristics alone does not guarantee fairness, because other fields can act as proxies. The best answer is to use a training sample representative of the target UK population.


NEW QUESTION # 71
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