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Microsoft AI-901 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Describe features of generative AI workloads on Azure (20-25%)	20-25%	- Identify features of generative AI solutions 1. Identify responsible AI considerations for generative AI 2. Identify features of generative AI models 3. Identify common scenarios for generative AI - Identify generative AI services and capabilities in Microsoft Azure 1. Describe features and capabilities of Azure AI Foundry model catalog 2. Describe features and capabilities of Azure OpenAI service 3. Describe features and capabilities of Azure AI Foundry
Topic 2: Describe Artificial Intelligence workloads and considerations (15-20%)	15-20%	- Identify features of common AI workloads 1. Identify features of knowledge mining workloads 2. Identify features of document intelligence workloads 3. Identify features of Natural Language Processing (NLP) workloads 4. Identify features of generative AI workloads 5. Identify features of computer vision workloads 6. Identify features of content moderation and moderation workloads - Identify guiding principles for responsible AI 1. Describe considerations for reliability and safety in an AI solution 2. Describe considerations for fairness in an AI solution 3. Describe considerations for transparency in an AI solution 4. Describe considerations for accountability in an AI solution 5. Describe considerations for privacy and security in an AI solution 6. Describe considerations for inclusiveness in an AI solution
Topic 3: Describe fundamental principles of machine learning on Azure (15-20%)	15-20%	- Identify common machine learning techniques 1. Identify clustering machine learning scenarios 2. Identify classification machine learning scenarios 3. Identify regression machine learning scenarios 4. Identify features of deep learning techniques - Describe Azure Machine Learning capabilities 1. Describe model management and deployment capabilities in Azure Machine Learning 2. Describe capabilities of automated machine learning 3. Describe data and compute services for data science and machine learning - Describe core machine learning concepts 1. Identify features and labels for training data in machine learning 2. Describe how training and validation datasets are used in machine learning
Topic 4: Describe features of computer vision workloads on Azure (15-20%)	15-20%	- Identify common types of computer vision solution 1. Identify features of semantic segmentation solutions 2. Identify features of face detection and identification solutions 3. Identify features of image analysis solutions 4. Identify features of optical character recognition (OCR) solutions 5. Identify features of image classification solutions 6. Identify features of object detection solutions - Identify Azure tools and services for computer vision tasks 1. Describe capabilities of the Azure AI Face detection service 2. Describe capabilities of the Azure AI Vision service
Topic 5: Describe features of Natural Language Processing (NLP) workloads on Azure (15-20%)	15-20%	- Identify features of common NLP workload scenarios 1. Identify features and uses for language modeling 2. Identify features and uses for key phrase extraction 3. Identify features and uses for entity recognition 4. Identify features and uses for sentiment analysis 5. Identify features and uses for speech recognition and synthesis 6. Identify features and uses for translation - Identify Azure tools and services for NLP workloads 1. Describe capabilities of the Azure AI Language service 2. Describe capabilities of the Azure AI Speech service

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Microsoft Azure AI Fundamentals Sample Questions (Q38-Q43):

NEW QUESTION # 38
You are developing an application that records a user's voice and sends the recorded audio to a deployed multimodal model in Microsoft Foundry.
You need to send the user's request to the model.
What should you include in the request?

A. a text embedding vector
B. an image of the audio frames
C. an audio input prompt
D. a text transcript of the voice

Answer: C

Explanation:
An audio input prompt must be included in the request when sending recorded voice directly to a native multimodal audio/speech model deployed in Microsoft Foundry.
Audio input prompt: Native multimodal models (such as audio-enabled GPT series deployed via Azure OpenAI in Microsoft Foundry Models) accept raw or base64-encoded audio directly as an input content type/modality in the inference payload.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/openai/audio-completions-quickstart

NEW QUESTION # 39
You plan to develop an AI application that will read the license plates of motor vehicles by using Azure AI Foundry. What should you use to develop the application?

A. GitHub Actions
B. Copilot for Microsoft 365
C. Azure AI Studio
D. Microsoft Visual Studio Code

Answer: C

Explanation:
Azure AI Studio (now Azure AI Foundry) is used to develop an AI application for reading license plates using its tools and models. The platform allows developers to build, customize, and deploy AI agents and applications, including those that process images to extract information like license plate numbers.
Key components of the process
Platform: Azure AI Foundry (formerly Azure AI Studio) is the central platform for building and managing AI applications.
Tools: You can use its integrated development environment (IDE), SDKs, and APIs to develop and customize models.
Models: The platform provides access to a wide range of AI models, including computer vision models capable of optical character recognition (OCR), which can be used to read text from images.
Workflow:
Create an Azure AI Foundry project in Azure.
Use the model catalog to find and deploy a suitable computer vision or OCR model.
Integrate the model into your application using the SDK or API.
For a license plate application, you would feed images to the model, and the model would output the recognized text from the license plate.
Reference:
https://azure.microsoft.com/en-us/products/ai-foundry

NEW QUESTION # 40
You are developing an application that extracts structured information from different types of content by using Azure Content Understanding in Foundry Tools.
You need to extract scanned invoices in the PDF format and voicemail recordings in the WAV format.
Which type of analyzer should you use for each content type? To answer, drag the appropriate analyzer types to the correct content types. Each analyzer type may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

Azure Content Understanding analyzers define the type of content to process, such as documents, images, audio, or video, and what to extract, such as text, layout, fields, transcripts, and structured JSON output.
Microsoft lists base analyzer types such as prebuilt-document, prebuilt-audio, prebuilt-video, and prebuilt- image.
For scanned invoices in PDF format, the correct choice is document analyzer, because invoices are document
/form content. Microsoft's Content Understanding quickstart specifically uses the prebuilt-invoice analyzer to extract structured data from an invoice PDF.
For voicemail recordings in WAV format, the correct choice is audio analyzer, because voicemail is audio content. Content Understanding supports structured data extraction from audio files, and the analyzer reference identifies prebuilt-audio as the base analyzer for audio content.

NEW QUESTION # 41
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: Yes
Yes - Whisper models in Azure OpenAI Service are used to convert speech to text.
The Whisper model is a speech to text model from OpenAI that you can use to transcribe or translate audio files. The model is trained on a large dataset of English audio and text. The model is optimized for transcribing audio files that contain speech in English.
Box 2: Yes
Yes - Embedding models in Azure OpenAI Service are used to convert text into numeric vectors.
Azure OpenAI Embeddings are a way to convert text data into numerical representations that capture semantic meaning. This allows you to find similar text content or compare documents based on their ideas, even if the wording differs.
Embeddings: a set of models that can convert text into numerical vector form to facilitate text similarity.
Box 3: No
GPT-4.1 models can understand and generate natural language or code. The most capable and cost effective model in the GPT-4.1 family is GPT-4.1 Turbo, which has been optimized for chat and works well for traditional completions tasks as well. GPT-4.1 Turbo is available for use with the Chat Completions API.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/speech-service/whisper-overview
https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models

NEW QUESTION # 42
You have a Python application that extracts fields from invoices by using Azure Content Understanding in Foundry Tools You submit a PDF for analysts.
What should the application do to retrieve the results?

A. Use optical character recognition (OCR) to parse the text from the PDF and map the fields in the code.
B. Poll the operation results until the asynchronous analysis job succeeds
C. Send a GET request to the Microsoft Foundry endpoint to download the analyzer schema.
D. Repeatedly call begin_analyze() with the same inputs to force a synchronous response.

Answer: B

Explanation:
Azure Content Understanding analysis operations are asynchronous long-running operations. Microsoft's Python SDK documentation states that analysis starts with a begin operation, then the application polls for results until analysis completes.
Therefore, after submitting the PDF for analysis, the application should poll the operation results until the asynchronous analysis job succeeds .

NEW QUESTION # 43
......

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