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Microsoft AI-901 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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. Language detection 3. Sentiment analysis
Topic 2: Describe features of computer vision workloads on Azure	15-20%	- Image analysis 1. Object detection 2. Optical character recognition (OCR) 3. Image classification - Azure AI Vision services 1. Face detection and analysis (limited use cases) 2. Azure AI Vision
Topic 3: Describe Artificial Intelligence workloads and considerations	20-25%	- Responsible AI principles 1. Privacy and security 2. Transparency and accountability 3. Inclusiveness 4. Reliability and safety 5. Fairness - Identify features of common AI workloads 1. Document processing and extraction 2. Computer vision use cases 3. Conversational AI and chatbots 4. Natural language processing use cases
Topic 4: 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 5: Describe features of generative AI workloads on Azure	10-15%	- Azure OpenAI and generative services 1. Azure OpenAI Service capabilities 2. Use cases: chatbots, content generation, summarization - 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 (Q12-Q17):

NEW QUESTION # 12
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 # 13
You have a Microsoft Foundry project that has a generative AI model deployment.
You need to ensure that responses generated by the model minimize costs and remain within a defined length.
Which parameter should you configure?

A. Temperature
B. Top P
C. Max Completion Tokens
D. Model version settings

Answer: C

Explanation:
The Max Completion Tokens (or max_tokens / max_output_tokens in the API) parameter is exactly the tool you use to enforce a fixed length on AI-generated responses and keep costs predictable.
How the Parameter Works
What it does: It sets a strict upper bound on the number of output tokens the model can generate for your completion. Once this number is reached, the model will stop generating (cut off) regardless of whether it finished its thought.
What it doesn't do: It does not set a minimum length or force the model to write exactly that amount. It only acts as a ceiling.
Reference:
https://learn.microsoft.com/en-us/answers/questions/5828812/reasoning-models-like-5-1-fails-with-500-error-cod

NEW QUESTION # 14
In the Microsoft Foundry portal, you create an agent named Agent1, and then deploy the agent.
You open the Foundry playground to test Agent1.
What does the playground use to ensure that the tests reflect production behavior?

A. a default configuration generated by the playground
B. the last saved configuration draft that was NOT deployed
C. the model's base behavior without any agent instructions or tools
D. the configuration of Agent1

Answer: D

Explanation:
The Configuration of the Agent: When you open the playground for a deployed agent in the Microsoft Foundry portal, it connects directly to the active deployment and uses the actual configuration, version, model, and tool bindings of the deployed agent to mirror real production logic.
Reference:
https://learn.microsoft.com/en-us/answers/questions/5564123/how-to-enable-the-agents-playground-feature-in-azu

NEW QUESTION # 15
Select the answer that correctly completes the sentence.

Answer:

Explanation:

NEW QUESTION # 16
You are developing a lightweight application that will call an agent programmatically by using the Microsoft Foundry SDK. You need to identify the agent ID. What should you do?

A. the Foundry portal, copy the project name.
B. In the Foundry tool catalog, copy the tool name used by the agent.
C. In the Foundry playground, select Code, and view the .env variables.
D. In the model catalog, open the model card and copy the model family name.

Answer: C

Explanation:
When calling a Foundry agent programmatically by using the Microsoft Foundry SDK, the application needs the agent's configuration values, including the agent ID . The Foundry playground provides a Code view that shows sample application code and environment variables needed to call the agent.
The other options are incorrect:
A). copy the tool name used by the agent identifies a tool, not the agent.
B). copy the model family name identifies the model family, not the agent instance.
C). copy the project name identifies the project, not the specific agent ID.

NEW QUESTION # 17
......

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