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>> Latest AI-901 Exam Question <<
Dumpleader offers up to 1 year of free Microsoft Azure AI Fundamentals (AI-901) exam questions updates. With our actual questions, you can prepare for the AI-901 exam without missing out on any point you need to know. These exam questions provide you with all the necessary knowledge that you will need to clear the Microsoft Azure AI Fundamentals (AI-901) exam with a high passing score.
NEW QUESTION # 107
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: A,B,C
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 # 108
Hotspot Question
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
Box: reliability and safety
When developing an AI system for self-driving cars, the Microsoft __________ principle for responsible AI should be applied to ensure consistent operation of the system during its lifespan.
The Microsoft Reliability and Safety principle for responsible AI should be applied to self-driving cars to ensure consistent operation and mitigate risks. This principle mandates that AI systems must be developed to be dependable and perform as intended, even when facing unexpected situations, making it crucial for a system where a minor error could have severe outcomes.
Consistent operation: Self-driving car AI must operate reliably and consistently under both normal and unexpected conditions.
Robust safety measures: The principle requires rigorous testing and validation to ensure the system responds safely to edge cases and avoids unexpected performance failures.
Preventing harm: The goal is to avoid creating harm by developing the system in alignment with its design ideas and values, which includes resisting manipulation.
Reference:
https://support.microsoft.com/en-us/topic/what-is-responsible-ai-33fc14be-15ea-4c2c-903b-aa493f5b8d92
NEW QUESTION # 109
You need to deploy a generative AI model to a Microsoft Foundry project. Which factor should you use to identify which type of model can be deployed to the project?
Answer: D
Explanation:
Yes, Azure region availability determines whether you can use a Global, Data Zone, or Regional/Standard deployment type for your generative AI model.
Deployment Types and Region Dependencies
Global Deployments (Global Standard / Global Provisioned): Use these when there are no strict single-region data residency constraints; traffic and inferencing may route to any Azure region where the model is actively supported and has capacity.
Data Zone Deployments (Data Zone Standard / Data Zone Provisioned): Use these when data processing must be restricted to a specific compliance boundary (such as the EU or US data zone), requiring your project to reside in a region belonging to that designated zone.
Regional Deployments (Standard / Regional Provisioned): Use these when your workload requires data processing to stay strictly within the single Azure region associated with your project.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/reference/region-support
NEW QUESTION # 110
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:
Explanation:
Statement 1: Evaluators in Microsoft Foundry replace the need for configuring token limits. = No Evaluators are used to assess model or agent output quality, safety, and reliability. They do not replace model configuration settings such as token limits. Microsoft describes evaluators as tools that measure the quality, safety, and reliability of AI responses.
Statement 2: Evaluators in Microsoft Foundry can assess the quality and safety of responses generated by a generative AI model. = Yes This is correct. Microsoft Foundry includes built-in evaluators for general quality metrics such as coherence and fluency, safety/security metrics, RAG metrics, and agent-specific metrics.
Statement 3: Evaluators in Microsoft Foundry can retrain a deployed generative AI model automatically when quality issues are detected. = No Evaluators measure and report quality, safety, and reliability issues. They do not automatically retrain deployed generative AI models. Microsoft describes evaluation as measuring model or agent performance against test data, while monitoring can alert when outputs fail quality thresholds.
NEW QUESTION # 111
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.
Answer:
Explanation:
Explanation:
Box 1: Yes
Yes - Whisper models in Azure OpenAI Service are used to convert speech to text.
The Whisper model is a speech to text model from OpenAI that you can use to transcribe or translate audio files. The model is trained on a large dataset of English audio and text. The model is optimized for transcribing audio files that contain speech in English.
Box 2: Yes
Yes - Embedding models in Azure OpenAI Service are used to convert text into numeric vectors.
Azure OpenAI Embeddings are a way to convert text data into numerical representations that capture semantic meaning. This allows you to find similar text content or compare documents based on their ideas, even if the wording differs.
Embeddings: a set of models that can convert text into numerical vector form to facilitate text similarity.
Box 3: No
GPT-4.1 models can understand and generate natural language or code. The most capable and cost effective model in the GPT-4.1 family is GPT-4.1 Turbo, which has been optimized for chat and works well for traditional completions tasks as well. GPT-4.1 Turbo is available for use with the Chat Completions API.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/speech-service/whisper-overview
https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models
NEW QUESTION # 112
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