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Microsoft AI-901 Exam Syllabus Topics:

Section	Weight	Objectives
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. Inclusiveness 2. Fairness 3. Privacy and security 4. Transparency and accountability 5. Reliability and safety
Describe fundamental principles of machine learning on Azure	25-30%	- Machine learning concepts 1. Supervised vs unsupervised learning 2. Training and evaluation concepts 3. Regression and classification - Azure Machine Learning basics 1. Azure ML workspace components 2. AutoML concepts
Describe features of natural language processing (NLP) workloads on Azure	15-20%	- Text analysis 1. Sentiment analysis 2. Language detection 3. Key phrase extraction - Azure NLP services 1. Azure AI Language 2. Speech services (speech-to-text, text-to-speech)
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
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. Responsible use of generative AI 2. Large language models (LLMs) 3. Prompt engineering basics

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Microsoft Azure AI Fundamentals Sample Questions (Q13-Q18):

NEW QUESTION # 13
You have a Microsoft Foundry project that contains a vision-enabled model deployment.
You use the Azure OpenAI Responses API to send a prompt to the model.
You need to provide an image for analysis.
Which content item should you include in the request?

A. image_generation
B. image_base64
C. input_image
D. output_image

Answer: C

Explanation:
When sending an image for analysis using the Azure OpenAI Responses API within a Microsoft Foundry project, you must include a content item object with the type input_image in the message payload.
Required Content Object Structure
Unlike the standard Chat Completions API (which uses type: "image_url"), the Responses API explicitly structures a multimodal image input as a separate block with the following properties:
type: Must be explicitly set to "input_image".
image_url: A dictionary or a direct parameter containing the target location of the image.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/openai/how-to/responses

NEW QUESTION # 14
You need to build an AI solution that generates marketing email drafts based on a short description of a product and its target audience. Which AI workload should you use?

A. text classification
B. generative AI
C. speech recognition
D. computer vision

Answer: B

Explanation:
A Generative AI workload is exactly what should be used for this scenario. Generative AI models (such as LLMs) are specifically designed to interpret prompts and create original, context-aware content, making them the perfect fit for drafting tailored marketing emails based on a product description and target audience.
Reference:
https://azure.microsoft.com/en-us/products/ai-foundry/tools/content-understanding

NEW QUESTION # 15
You have a multimodal model deployed in Microsoft Foundry
You need to configure the model to analyze an image and provide both a written summary and an audio summary of the image.
What is the minimum number of user prompts that should be sent to the model?

A. 0
B. A
C. 1
D. 2

Answer: A

Explanation:
A multimodal model can receive a single user prompt that includes the image plus instructions. The prompt can ask for both outputs in one request, for example: "Analyze this image and provide a written summary and an audio summary." Therefore, the minimum number of user prompts is 1 .

NEW QUESTION # 16
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 # 17
Hotspot Question
Select the answer that correctly completes the sentence.

Answer:

Explanation:

NEW QUESTION # 18
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