2026 Microsoft First-grade AI-901: Microsoft Azure AI Fundamentals Exam Lab Questions

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Microsoft AI-901 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Describe Artificial Intelligence workloads and considerations	20-25%	- Identify features of common AI workloads 1. Conversational AI and chatbots 2. Natural language processing use cases 3. Computer vision use cases 4. Document processing and extraction - Responsible AI principles 1. Privacy and security 2. Reliability and safety 3. Fairness 4. Transparency and accountability 5. Inclusiveness
Topic 2: Describe features of computer vision workloads on Azure	15-20%	- Image analysis 1. Object detection 2. Optical character recognition (OCR) 3. Image classification - Azure AI Vision services 1. Azure AI Vision 2. Face detection and analysis (limited use cases)
Topic 3: Describe fundamental principles of machine learning on Azure	25-30%	- Machine learning concepts 1. Regression and classification 2. Supervised vs unsupervised learning 3. Training and evaluation concepts - Azure Machine Learning basics 1. AutoML concepts 2. Azure ML workspace components
Topic 4: Describe features of generative AI workloads on Azure	10-15%	- Generative AI concepts 1. Prompt engineering basics 2. Responsible use of generative AI 3. Large language models (LLMs) - Azure OpenAI and generative services 1. Azure OpenAI Service capabilities 2. Use cases: chatbots, content generation, summarization
Topic 5: Describe features of natural language processing (NLP) workloads on Azure	15-20%	- Text analysis 1. Sentiment analysis 2. Language detection 3. Key phrase extraction - Azure NLP services 1. Azure AI Language 2. Speech services (speech-to-text, text-to-speech)

>> AI-901 Exam Lab Questions <<

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Microsoft Azure AI Fundamentals Sample Questions (Q71-Q76):

NEW QUESTION # 71
You are building a knowledge base by using Azure AI Language service's custom answering feature. Which file format can you use to populate the knowledge base?

A. PPTX
B. JPEG
C. PDF
D. ZIP

Answer: C

Explanation:
Azure AI Language service's custom answering can populate its knowledge base using various file formats, including structured text (.txt, .tsv), Microsoft Office documents (.docx, .xlsx, .pptx), and HTML, PDF, and Markdown files.
Reference:
https://docs.azure.cn/en-us/ai-services/language-service/question-answering/reference/document-format-guidelines

NEW QUESTION # 72
What are two types of generative AI models? Each correct answer presents a complete solution.
NOTE: Each correct answer is worth one point

A. transformer
B. natural language processing
C. computer vision
D. GPT
E. DALL-E

Answer: D,E

Explanation:
GPT (Generative Pre-trained Transformer): A generative AI model that focuses on text-based tasks, such as language generation, summarization, and question answering. GPT models are foundational for tasks involving natural language generation.
DALL-E: A generative AI model designed to create images from textual descriptions. It represents a type of generative model specialized in computer vision tasks, specifically generating new visual content.

NEW QUESTION # 73
Hotspot Question
Select the answer that correctly completes the sentence.

Answer:

Explanation:

NEW QUESTION # 74
You are developing an application that extracts fields from PDFs by using Azure Content Understanding in Foundry Tools.
You need to use the Python SDK to submit a PDF for analysis and retrieve the extraction results.
What should you do?

A. Call analyze() to return the extracted fields synchronously in the same request.
B. Submit the PDF to an analyzer and read the results from the request headers.
C. Call begin_analyze(), and then call poller.result() to retrieve the results.
D. Use optical character recognition (OCR) to extract text from the PDF and map the fields in the code.

Answer: C

Explanation:
Azure Content Understanding analysis operations are long-running operations in the Python SDK. Microsoft's Python SDK documentation states that analysis operations return a poller, and the SDK provides LROPoller types that handle polling automatically when you call .result().
Therefore, the correct workflow is to submit the PDF by calling begin_analyze(), receive a poller, and then call:
result = poller.result()
Option B is incorrect because extraction results are not read from request headers.
Option C is incorrect because the requirement is to use Azure Content Understanding extraction, not build a manual OCR-only mapping pipeline.
Option D is incorrect because the SDK analysis pattern is asynchronous/long-running, not a simple synchronous analyze() call that returns all extracted fields in the same request.

NEW QUESTION # 75
You need to compare the costs of large language models (LLMs) for a generative AI solution.
What should you use in the Microsoft Foundry portal?

A. Tools
B. Compliance
C. Evaluator catalog
D. Model leaderboard

Answer: D

Explanation:
To compare the costs of large language models in Microsoft Foundry portal, use the Model leaderboard.
Microsoft documentation states that the model leaderboard helps compare models across quality, safety, estimated cost, and throughput. It also supports trade-off charts and side-by-side model comparison for features, performance, and estimated cost.
Why the other options are incorrect:
A . Evaluator catalog is for selecting evaluators to measure model or application outputs, not comparing LLM costs.
C . Compliance relates to governance and compliance, not model cost comparison.
D . Tools provides Foundry tools, not benchmarked cost comparison across models.

NEW QUESTION # 76
......

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