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Microsoft AI-901 Exam Syllabus Topics:

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

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Microsoft Azure AI Fundamentals Sample Questions (Q32-Q37):

NEW QUESTION # 32
Hotspot Question
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation:
Box: a multimodal
Processing prompts that contain images requires you to deploy and use a multimodal model (such as GPT-4o) via Microsoft Foundry.
Reference:
https://learn.microsoft.com/en-us/azure/foundry-classic/how-to/flow-process-image

NEW QUESTION # 33
For each of the following statements, select if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 34
You plan to create an AI application that will use Azure OpenAI Service. The solution requires that a specific amount of throughput be allocated. Which type of deployment should you use?

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

Answer: B

Explanation:
When a specific amount of throughput is needed with Azure OpenAI Service, you should use Provisioned Throughput Units (PTUs) for your deployment. PTUs allow you to reserve a fixed amount of processing capacity for your models, ensuring consistent performance and avoiding potential throttling or rate limits.
In contrast, the "Standard" deployment option in Azure OpenAI Service uses a "pay-as-you-go" model, where you are billed for the amount of usage. While this can be suitable for applications with fluctuating usage patterns, it does not provide the same level of throughput control as PTUs.
In summary: When you need to allocate a specific amount of throughput with Azure OpenAI Service, choose the "Provisioned" deployment type, which allows you to use PTUs.
Reference:
https://github.com/MicrosoftDocs/azure-ai-docs/blob/main/articles/ai-services/openai/concepts/provisioned-throughput.md

NEW QUESTION # 35
You are creating an app to help employees write emails and reports based on user prompts.
What should you use?

A. Azure AI Vision
B. Azure AI Speech
C. Azure OpenAI Service
D. Azure Machine Learning studio

Answer: C

Explanation:
Azure OpenAI Service can generate text based on user prompts. It leverages GPT (Generative Pre-trained Transformer) models that are trained to understand and generate human-like text.
Users interact with these models by providing a prompt, and the model attempts to produce the most likely continuation or completion of that text.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models

NEW QUESTION # 36
You have a Microsoft Foundry project that contains a vision-enabled model deployment.
You need to develop an application that sends a message containing text and an image URL.
The solution must ensure the quickest response time.
Which message structure should you include in the request?

A. a system message that includes only a text item and sends an image item in a separate request
B. a system message that includes both a text item and an image item in the content array
C. a user message that includes both a text item and an image item in the content array
D. a user message that includes only a text item and sends an image item in a separate request

Answer: C

Explanation:
For a vision-enabled model, Microsoft documentation shows that multimodal prompts can include both text and image content in the same user message content array. Azure OpenAI vision models accept multimodal image-and-text input and return natural language responses.
The correct request structure is a user message with a content array containing both items, for example:
{
"role": "user",
"content": [
{"type": "input_text", "text": "What is in this image?"}, {"type": "input_image", "image_url":
image_url}
]
}
This avoids sending separate requests and is therefore the best choice for quickest response time.
A and C are incorrect because they send the text and image separately. B is incorrect because the image/question should be sent as a user message, while system messages are used for instructions and behavior.

NEW QUESTION # 37
......

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