100% Pass Quiz 2026 The Best Microsoft AI-901: Microsoft Azure AI Fundamentals Test Pattern

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Microsoft AI-901 Exam Syllabus Topics:

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

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Microsoft Azure AI Fundamentals Sample Questions (Q11-Q16):

NEW QUESTION # 11
You have a Microsoft Foundry project that contains a deployed generative Al model You need to develop an application by using the Microsoft Foundry SDK to send chat prompts to the deployed model Which information must the application include?

A. Exported playground session history, endpoint URL and task type
B. Credentials, endpoint URL, and model deployment name
C. Model training dataset. exported playground session history, and Foundry project display name
D. Foundry project display name, model training dataset. and task type

Answer: B

Explanation:
To send chat prompts to a deployed model, the application needs authentication credentials, the endpoint URL, and the model deployment name . Microsoft's Foundry SDK guidance shows authentication with credentials, an endpoint, and the model= " model_deployment_name " value when calling a deployed model.

NEW QUESTION # 12
Hotspot Question
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 # 13
Hotspot Question
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:

Explanation:
Box 1: No
No, batch transcription in Azure Speech does not use a microphone. It processes large volumes of prerecorded audio files stored in locations like Azure Blob Storage.
Box 2: No
No, Azure Speech in Foundry Tools cannot analyze video frames to detect or label objects. It is strictly an audio-focused tool designed for speech-to-text transcription, text-to-speech synthesis, speech translation, and voice analytics Box 3: No No, speech translation in Azure Speech in Foundry Tools outputs translated text as well as optional spoken audio.
Supported Output Formats
Text output: It can output the recognized speech as translated text in one or multiple target languages without generating any spoken audio.
Speech-to-Speech: It can optionally synthesize that translated text into natural-sounding audible speech (audio output) using text-to-speech capabilities.
Source transcription: It simultaneously provides the original source language transcription in text format alongside the translation results.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/speech-service/batch-transcription
https://learn.microsoft.com/en-us/azure/foundry/responsible-ai/speech-service/speech-to-text/transparency-note

NEW QUESTION # 14
Select the answer that correctly completes the sentence.

Answer:

Explanation:

NEW QUESTION # 15
You have a Microsoft Foundry project that contains a vision-enabled model deployment.
You are developing an application that sends images to the model.
You need to ensure that the model can analyze the images.
In which two formats can you provide the images? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A. a JSON document that describes the image content
B. a UTF-8 encoded text description of the image
C. a publicly accessible URL of the image
D. a base64-encoded image data string

Answer: C,D

Explanation:
Images can be sent to these models either by passing a publicly accessible image URL or by sending Base64-encoded image data directly in the message payload.
Reference:
https://learn.microsoft.com/en-us/azure/foundry-classic/foundry-models/how-to/use-chat-multi-modal

NEW QUESTION # 16
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