[Sep 28, 2026] Microsoft AI-901 Exam Dumps Are Essential To Get Good Marks [Q30-Q45]

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[Sep 28, 2026] Microsoft AI-901 Exam Dumps Are Essential To Get Good Marks

Latest Microsoft AI-901 Dumps with Test Engine and PDF (New Questions)

Microsoft AI-901 Exam Syllabus Topics:

Topic	Details
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.

NEW QUESTION 30
Based on the image provided, here is the transcribed text:
You need to build an AI solution that produces new product images based on written descriptions provided by users.
Which AI workload should you use?

image generation

image analysis

object detection

optical character recognition (OCR)

The requirement is to produce new product images based on written descriptions. This is an image generation workload, because the AI system is creating entirely new images from natural language prompts.
Why the other options are incorrect:
B . image analysis is used to examine and interpret existing images.
C . object detection is used to identify and locate objects within an existing image.
D . optical character recognition (OCR) is used to extract text from images or scanned documents.
Since the solution must generate new visual content from user-provided descriptions, the correct answer is:
A . image generation

NEW QUESTION 31
Drag and Drop Question
You are reviewing best practices for using AI at your company.
Which Microsoft responsible AI principle is each task an example of? To answer, drag the appropriate principles to the correct tasks. Each task may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct match is worth one point.

NEW QUESTION 32
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

NEW QUESTION 33
Drag and Drop Question
You have a Microsoft Foundry project named project1 that contains an Azure OpenAI resource named Resource1.
To Resource1, you deploy a gpt-4.1-mini model by using a model deployment named my-mini- gpt.
You need to connect to my-mini-gpt from an application.
How should you complete the Python code? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.

NEW QUESTION 34
You are creating an app to help employees write emails and reports based on user prompts.
What should you use?

Azure AI Vision

Azure Machine Learning studio

Azure OpenAI Service

Azure AI Speech

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 35
Drag and Drop Question
You are developing an application that extracts structured information from different types of content by using Azure Content Understanding in Foundry Tools.
You need to extract scanned invoices in the PDF format and voicemail recordings in the WAV format.
Which type of analyzer should you use for each content type? To answer, drag the appropriate analyzer types to the correct content types. Each analyzer type may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

NEW QUESTION 36
Hotspot Question
Select the answer that correctly completes the sentence.

NEW QUESTION 37
Which Azure Machine Learning capability should you use to quickly build and deploy a predictive model without extensive coding?

ML pipelines

Copilot

DALL-E

automated machine learning (automated ML)

To easily build and deploy a predictive model using Azure Machine Learning, start by selecting the appropriate tools for your task, such as Azure Machine Learning Studio or AutoML, and follow a streamlined process of data preparation, model training, and deployment.
AutoML stands for Automated Machine Learning. It’s a process that automates the various tasks involved in building machine learning models, including data preparation, feature selection, model training, hyperparameter tuning, and model evaluation, according to IBM. This automation makes machine learning more accessible to a wider range of users, including those without extensive machine learning expertise.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/concept-automated-ml?view=azureml-api-2

NEW QUESTION 38
Hotspot Question
You are developing a voice application that listens for spoken commands and converts them into text by using Azure Speech in Foundry Tools.
How should you complete the Python code? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.

NEW QUESTION 39
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.

NEW QUESTION 40
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?

Top P

Temperature

Max Completion Tokens

Model version settings

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 41
Hotspot Question
You are developing a voice application that listens for spoken commands and converts them into text by using Azure Speech in Foundry Tools.
How should you complete the Python code? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.

NEW QUESTION 42
Hotspot Question
Select the answer that correctly completes the sentence.

NEW QUESTION 43
Drag and Drop Question
You have a Microsoft Foundry project that contains a model deployment.
You are developing an application that sends an image and a user question to the model.
You need to send both text and image content in the same request so the model can return an answer.
How should you complete the Python code? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

NEW QUESTION 44
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?

global batch

provisioned

data zone standard

standard

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 45
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.

voting

computer vision

stacking

classification

regression

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

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[Sep 28, 2026] Microsoft AI-901 Exam Dumps Are Essential To Get Good Marks [Q30-Q45]

Microsoft AI-901 Exam Syllabus Topics:

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Microsoft AI-901 Exam Syllabus Topics:

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