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Microsoft AI-901 Exam Overview:

Certification Vendor:Microsoft
Exam Name:Microsoft Azure AI Fundamentals (AI-900) Exam
Exam Number:AI-900
Real Exam Qty:40-60
Exam Duration:60 minutes
Exam Price:Approx. 99 USD (varies by region)
Passing Score:700 (out of 1000)
Certificate Validity Period:Does not expire (Fundamentals certification)
Related Certifications:Microsoft Azure Fundamentals (AZ-900)
Microsoft Azure Data Fundamentals (DP-900)
Available Languages:Spanish, Chinese (Traditional), Portuguese (Brazil), Korean, German, Chinese (Simplified), Japanese, French, English
Exam Format:Multiple choice, Case study (limited), Multiple response, Drag and drop
Recommended Training:Azure AI Fundamentals Course
Microsoft Learn - AI-900 Learning Path
Exam Registration:Schedule exam via Pearson VUE
Microsoft Certification Portal
Sample Questions:Microsoft AI-901 Sample Questions
Exam Way:Online proctored exam or in-person test center
Pre Condition:No formal prerequisites required. Basic understanding of cloud computing and AI concepts is recommended.
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/certifications/azure-ai-fundamentals/

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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.

Microsoft Azure AI Fundamentals Sample Questions (Q109-Q114):

NEW QUESTION # 109
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation:

The completed sentence is:
After deploying a vision-enabled GPT model in Microsoft Foundry, you can configure an application to send requests to the endpoint of the model .
To call a deployed model from an application, the application must send requests to the deployed model's endpoint and include the required authentication credentials. The Foundry playground is used for testing interactively, but application code calls the deployed model endpoint.
The other options are incorrect:
evaluation pipeline of model is used to evaluate model performance, not to receive inference requests from an app.
Foundry playground is an interactive testing environment, not the production target for application requests.
training dataset of the model is used for training or fine-tuning, not for sending inference requests.
Therefore, the correct answer is endpoint of the model .


NEW QUESTION # 110
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: C


NEW QUESTION # 111
Hotspot Question
Select the answer that correctly completes the sentence.

Answer:

Explanation:


NEW QUESTION # 112
You are using the Azure Speech SDK to develop a Python application that supports real-time spoken conversations. Which Azure speech class should you use to configure the connection to the Azure Speech service?

Answer: C


NEW QUESTION # 113
You are developing a simple application that uses the Microsoft Foundry SDK to send chat prompts to a model.
Which three elements should you provide in the code to run the application? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

Answer: B,C,E

Explanation:
To build a simple application that sends chat prompts to a model using the Microsoft Foundry SDK, you will initialize a project client using your endpoint and credentials, then request chat completions using your model deployment name.
Microsoft Entra ID Credentials: Handled via DefaultAzureCredential(). This eliminates the need to hardcode sensitive API keys. Before running this app, ensure you are authenticated in your terminal by running az login via the Azure CLI. Your account must have the Cognitive Services User or Azure AI Developer role assigned.
Endpoint URL: Passed directly into the AIProjectClient initializer. This maps your code to the specific Azure AI Foundry project hub managing your computational resources.
Model Deployment Name: Referenced inside the final .create() method payload. This instructs the inference routing service exactly which deployed LLM (e.g., GPT-4o, DeepSeek, or Claude) should process your incoming chat transcript.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/openai/how-to/chatgpt


NEW QUESTION # 114
......

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