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Microsoft AI-901 Exam Syllabus Topics:

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

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Microsoft Azure AI Fundamentals Sample Questions (Q94-Q99):

NEW QUESTION # 94
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation:

When content is submitted to Azure Content Understanding in Foundry Tools, the analysis is asynchronous .
This means the service does not return results immediately within the same HTTP request. Instead, it uses the standard Azure long-running operation (LRO) pattern - you call begin_analyze() to submit the content, which immediately returns a poller object, and then call poller.result() to wait for processing to complete and retrieve the structured extraction results.
Why the other options are wrong:
* Synchronous is incorrect - the analysis pipeline involves multiple AI steps (OCR, speech transcription, schema mapping) that take time; a blocking synchronous call is not supported.
* Returned only as unstructured plain text is incorrect - Azure Content Understanding returns richly structured JSON output with named fields mapped to your defined schema, not plain unstructured text.
* Limited to OCR-only processing is incorrect - Content Understanding goes far beyond OCR; it supports document, audio, image, and video analyzers, and performs semantic field extraction using AI, not just character recognition.
This asynchronous design is consistent across all Azure AI services that perform complex, multi-step content processing.

NEW QUESTION # 95
You have a Python application that extracts fields from invoices by using Azure Content Understanding in Foundry Tools.
You submit a PDF for analysis.
What should the application do to retrieve the results?

A. Repeatedly call begin_analyze() with the same inputs to force a synchronous response.
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. Use optical character recognition (OCR) to parse the text from the PDF and map the fields in the code.

Answer: B

Explanation:
To retrieve the extraction results using the official azure-ai-contentunderstanding Python SDK, the application must call begin_analyze(), and then call poller.result() to retrieve the results.
Because document analysis is an asynchronous operation, the process is split into initiating the request and waiting for the long-running operation to complete.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/quickstart/use-rest-api

NEW QUESTION # 96
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation:

The Microsoft responsible AI principle of transparency requires that AI systems be explainable to users
.
Transparency means users should understand when they are interacting with AI, what the system can and cannot do, and how outputs or predictions are generated at an appropriate level.
The other options map to different responsible AI principles:
protect sensitive user data = Privacy and security
reduce bias in decisions = Fairness
require human oversight = Accountability

NEW QUESTION # 97
You plan to create an Al application that will use Azure OpenAI in Foundry Models. The solution requires that a specific amount of throughput be allocated.
Which type of deployment should you use?

A. standard
B. provisioned
C. global batch
D. data zone standard

Answer: B

NEW QUESTION # 98
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:

Statement 1: Azure Content Understanding in Foundry Tools can analyze only PDF documents. = No Azure Content Understanding is not limited to PDF documents. It can analyze different content types, including documents, forms, images, audio, and video.
Statement 2: Azure Content Understanding in Foundry Tools results are returned in the JSON format. = Yes Azure Content Understanding returns structured analysis output in JSON format.
Statement 3: Azure Content Understanding in Foundry Tools can extract structured fields from documents and forms. = Yes Azure Content Understanding can extract structured fields from documents and forms by using analyzers and schemas.

NEW QUESTION # 99
......

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