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Microsoft AI-901 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Identify AI concepts and capabilities: This domain covers the foundational knowledge of AI from ethical principles and responsible design to understanding how AI models work and what kinds of tasks they can perform. It also explores the full range of AI workloads including generative AI, computer vision, speech, and information extraction.
Topic 2
  • Implement AI solutions by using Microsoft Foundry: This domain is hands-on and focuses on building and deploying AI solutions using the Microsoft Foundry platform and its associated tools. It spans generative AI apps, text and speech processing, computer vision, and document intelligence all implemented through the Foundry portal and SDK.

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Microsoft Azure AI Fundamentals Sample Questions (Q111-Q116):

NEW QUESTION # 111
In Azure Machine Learning, what are two ensemble methods for combining models in automated machine learning (automated ML)? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

Answer: C,D

Explanation:
In Azure Machine Learning's automated machine learning (AutoML), two prominent ensemble methods for combining models are Voting and Stacking.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/concept-automated-ml 1


NEW QUESTION # 112
You plan to deploy a generative AI model to a Microsoft Foundry project in the East US Azure region. What should you check first?

Answer: D

Explanation:
Model availability should be checked first. You must confirm that the specific generative AI model is supported and offered in the East US region before checking your token rate limits or allocation quota, as a model cannot be deployed in a region where it does not exist.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/foundry-models/quotas-limits


NEW QUESTION # 113
You need to create an AI agent in Microsoft Foundry that follows a specific role and behavior when responding to users.
What should you configure?

Answer: A

Explanation:
To create an AI agent that follows a specific role and behavior, you configure system instructions . Microsoft Foundry Agent Service documentation states that agent instructions define goals, constraints, and behavior
.
Option A. tokens per minute (TPM) controls throughput quota, not behavior.
Option C. temperature controls response randomness/creativity, not the agent's role.
Option D. max completion tokens controls response length, not the agent's role or behavioral rules.
Therefore, the correct answer is B. system instructions .


NEW QUESTION # 114
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation:
The correct answer is API endpoint. After a generative AI model is deployed in Microsoft Foundry, the deployment exposes an inference endpoint through which external applications can submit requests and receive generated responses. Microsoft Foundry documentation describes deployed models as being accessible programmatically through an endpoint and associated credentials. Applications invoke the relevant REST API or SDK against this endpoint and specify the deployed model as required by the API.
An embedding vector is a numerical representation of semantic information used for similarity search, retrieval, and grounding; it is not the network interface through which a deployed model is invoked. A training dataset contains data used to train or fine-tune a model and is not called when performing inference.
A URL parameter can be part of an API request, but it does not represent the deployed model ' s callable interface.
This directly aligns with the AI-901 objective to deploy a model and interact with it in the Foundry portal and to create lightweight applications that invoke deployed generative AI models through supported APIs and SDKs.


NEW QUESTION # 115
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 # 116
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