P.S. Free & New AI-901 dumps are available on Google Drive shared by iPassleader: https://drive.google.com/open?id=1KV37x79zXqqUZVqeMkXbYL1UDzjZpYXn
Because our AI-901 actual exam help exam cannonades pass the exam with rate up to 98 to 100 percent. It encourages us to focus more on the quality and usefulness of our AI-901 exam questions in the future. And at the same time, we offer free demos before you really choose our three versions of AI-901 Practice Guide. Time is flying, hope you can begin your review on our AI-901 study engine as quickly as possible.
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement AI solutions by using Microsoft Foundry | 55-60% | - Work with models in Microsoft Foundry
|
| Topic 2: Identify AI concepts and responsibilities | 40-45% | - Identify AI concepts and features
|
The AI-901 real questions are written and approved by our It experts, and tested by our senior professionals with many years' experience. The content of our AI-901 pass guide covers the most of questions in the actual test and all you need to do is review our AI-901 VCE Dumps carefully before taking the exam. Then you can pass the actual test quickly and get certification easily.
NEW QUESTION # 95
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.
Answer:
Explanation:
Explanation:
client = OpenAI(
api_key= " ... " ,
base_url= " https://resource1.openai.azure.com/openai/v1/ " ,
)
response = client.responses.create(
model= " my-mini-gpt " ,
)
For Azure OpenAI in Microsoft Foundry, the base_url uses the Azure OpenAI resource name in the endpoint format:
https:// < resource-name > .openai.azure.com/openai/v1/
In the question, the Azure OpenAI resource is named Resource1, so the first blank must be resource1.
Microsoft documentation for Azure OpenAI v1 endpoints confirms that the endpoint must use the ...openai.
azure.com/openai/v1/ path.
For the model parameter, Azure OpenAI requires the deployment name, not the underlying model name.
Microsoft states that Azure OpenAI always requires the deployment name when calling APIs, even when the parameter is named model.
The deployed model is gpt-4.1-mini, but the deployment name is my-mini-gpt. Therefore, the second blank must be:
model= " my-mini-gpt "
So the correct selections are:
base_url blank = resource1
model blank = my-mini-gpt
NEW QUESTION # 96
You are designing a system that will generate insurance quotes automatically.
Match the Microsoft responsible AI principles to the appropriate requirements.
To answer, drag the appropriate principle from the column on the left to its requirement on the right. Each principle may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
Answer:
Explanation:
Explanation:
Requirement
Principle
The decision-making process must be recorded so that staff can identify the reasoning behind a particular quote.
Transparency
A customer ' s personal information must be visible only to staff who are involved in the decision-making process.
Privacy and security
The system must be accessible to customers who use screen readers or other assistive technology.
Inclusiveness
Microsoft ' s Responsible AI framework defines six core principles: fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability.
The first requirement maps to Transparency because personnel must be able to understand and review how the AI system arrived at an insurance quote. Transparency concerns making AI behavior, capabilities, limitations, and decision processes understandable. Recording the decision-making process provides the traceability required to explain a particular result.
The second requirement maps to Privacy and security. Restricting customer information to authorized staff directly implements least-privilege access and protects confidential personal data from unauthorized disclosure. Microsoft specifically associates this principle with respecting data boundaries and safeguarding information handled by AI systems.
The third requirement maps to Inclusiveness. AI systems should accommodate the full range of people they serve, including individuals who rely on assistive technologies. Support for screen readers and accessibility technologies ensures that users with disabilities are not excluded from interacting with the system. Microsoft explicitly connects inclusive AI design with accessibility considerations for people with disabilities.
NEW QUESTION # 97
Your company has thousands of recorded customer support calls in multiple languages stored as audio files in Azure Storage.
You need to generate text transcripts of all the recordings.
Which Azure Speech in Foundry Tools capability should you use?
Answer: B
Explanation:
For this scenario, you should use the Speech-to-Text Batch Transcription feature in Azure AI Speech. It is specifically built to asynchronously transcribe large volumes of prerecorded audio files from Azure Blob Storage concurrently, reducing your overall turnaround time.
To address the requirement for multiple languages without knowing the language of each call in advance, you should configure the service to use an Automatic Multilingual Configuration (a multilingual model). This allows the Speech service to automatically detect and transcribe multiple spoken languages without requiring you to split files or submit language-specific requests.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/audio/overview
NEW QUESTION # 98
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?
Answer: D
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 # 99
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 # 100
......
If you are looking to advance in the fast-paced and technological world, iPassleader is here to help you achieve this aim. iPassleader provides you with the excellent Microsoft AI-901 practice exam, which will make your dream come true of passing the Microsoft Azure AI Fundamentals certification exam on the first attempt.
Real AI-901 Dumps Free: https://www.ipassleader.com/Microsoft/AI-901-practice-exam-dumps.html
P.S. Free 2026 Microsoft AI-901 dumps are available on Google Drive shared by iPassleader: https://drive.google.com/open?id=1KV37x79zXqqUZVqeMkXbYL1UDzjZpYXn