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Microsoft AI-901 Exam Syllabus Topics:

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

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The software is designed for use on a Windows computer. This software helps hopefuls improve their performance on subsequent attempts by recording and analyzing Microsoft Azure AI Fundamentals (AI-901) exam results. Like the actual Microsoft AI-901 certification exam, Microsoft Azure AI Fundamentals (AI-901) practice exam software has a certain number of questions and allocated time to answer. Any questions or concerns can be directed to the LatestCram support team, who are available 24/7. However, the Microsoft Azure AI Fundamentals (AI-901) exam questions software product license must be validated before use.

Microsoft Azure AI Fundamentals Sample Questions (Q117-Q122):

NEW QUESTION # 117
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 # 118
You have a Microsoft Foundry project named project1 that contains an Azure OpenAI resource named Resource1.
To Resource1, you deploy a gpt-4.1-mini model by using a model deployment named my-mini-gpt.
You need to connect to my-mini-gpt from an application.
How should you complete the Python code? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 119
You are building an AI-based loan approval app.
You need to ensure that the app documents why a loan is approved or rejected and makes the report available to the applicant.
This is an example of which Microsoft responsible AI principle?

A. inclusiveness
B. fairness
C. accountability
D. transparency

Answer: D

Explanation:
Transparency: AI systems should be transparent and understandable. AI systems might help inform decisions that have great impact on people's lives, so it's important that people understand how these decisions are made.
Reference:
https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/strategy/responsible-ai

NEW QUESTION # 120
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 # 121
Drag and Drop Question
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:

NEW QUESTION # 122
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