AI-901在線題庫，AI-901參考資料

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Microsoft AI-901 考試大綱：

主題簡介
主題 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.
主題 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.

>> AI-901在線題庫 <<

Microsoft AI-901參考資料 & AI-901熱門考題

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最新的 Microsoft Certified: Azure AI Fundamentals AI-901 免費考試真題 (Q52-Q57):

問題 #52
You are deploying a generative Al model to a Microsoft Foundry project. You assign a higher tokens per minute (TPM) allocation to the model. What is the result of this change?

答案：C

解題說明：
Tokens per minute, or TPM , is a throughput allocation for a model deployment. Assigning a higher TPM allocation increases the amount of token processing capacity available to the deployment, which affects the speed and scale at which the deployment can process requests.
It does not directly make responses longer, change region availability, or automatically add citations.


問題 #53
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?

答案：D

解題說明：
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


問題 #54
You are building a chatbot that will use natural language processing (NLP) to perform the following actions based on the text input of a user:
- Accept customer orders.
- Retrieve support documents.
- Retrieve order status updates.
Which type of NLP should you use?

答案：C

解題說明：
The correct choice is language understanding (also termed language modeling).
Language understanding allows a system to determine a user's intent from their natural language input. In this scenario, the chatbot needs to look at a text string and map it to one of three specific operational categories (intents):
Accepting a customer order
Retrieving support documentation
Checking an order status update


問題 #55
You deploy the Azure OpenAI service to generate images.
You need to ensure that the service provides the highest level of protection against harmful content.
What should you do?

答案：B

解題說明：
Azure OpenAI, Content filtering
Azure OpenAI includes a content filtering system that works alongside core models, including image generation models. This system works by running both the prompt and completion through a set of classification models designed to detect and prevent the output of harmful content.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/content-filter


問題 #56
Select the answer that correctly completes the sentence.

答案：

解題說明：

Explanation:
Speech
The correct answer is speech . The scenario specifically describes converting spoken customer requests into written text , which is a speech-to-text , or speech recognition, capability. Microsoft classifies speech as one of the common AI workloads in the current Azure AI Fundamentals study guide. The guide explicitly requires candidates to identify scenarios involving speech and to understand the capabilities of speech recognition and speech synthesis .
Microsoft Azure Speech in Foundry Tools provides speech-to-text functionality that converts audio streams into textual transcriptions. It supports real-time transcription, fast transcription, and batch transcription depending on the workload.
Key phrase extraction analyzes existing text to identify important words or concepts; it does not perform audio transcription. Text analysis covers operations such as sentiment analysis, entity recognition, summarization, and key phrase extraction after text already exists. Natural Language Understanding (NLU) interprets the meaning or intent of language, but the conversion from audio into written words itself is specifically a speech recognition workload .
Study Guide Reference: Identify AI concepts and capabilities # Identify AI workloads # Identify features and capabilities of speech recognition and speech synthesis.


問題 #57
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