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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Azure AI Fundamentals |
| Exam Number: | AI-901 |
| Exam Format: | Case studies, Multiple choice, Multiple select |
| Real Exam Qty: | 30-40 |
| Passing Score: | 700 |
| Available Languages: | Spanish, Italian, German, Portuguese (Brazil), Indonesian (Indonesia), Arabic (Saudi Arabia), Chinese (Traditional), Korean, English, French, Russian, Japanese, Chinese (Simplified) |
| Certificate Validity Period: | Lifetime |
| Exam Price: | $99 USD |
| Exam Duration: | 60 minutes |
| Recommended Training: | Microsoft Learn: Get started with Microsoft Foundry Microsoft Learn: AI concepts for developers and technology professionals |
| Exam Registration: | Microsoft Certification Exam Registration Pearson VUE Scheduling |
| Sample Questions: | Microsoft AI-901 Sample Questions |
| Exam Way: | Online proctored or onsite at Pearson VUE test centers |
| Pre Condition: | No required prerequisites; basic understanding of cloud computing and familiarity with Python syntax recommended |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/exams/ai-901/ |
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| Topic | Details |
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| Topic 2 |
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NEW QUESTION # 124
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 # 125
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?
Answer: B
Explanation:
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 # 126
Hotspot Question
Select the answer that correctly completes the sentence.
Answer:
Explanation:
NEW QUESTION # 127
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:
NEW QUESTION # 128
You have a Microsoft Foundry project that contains an agent named Agent1.
You need to ensure that Agent1 always calls an Azure function when the agent responds to user input.
To what should you set tool_choice for Agent1?
Answer: B
Explanation:
Microsoft's Foundry Agent Service documentation states that tool_choice provides deterministic control over tool calling:
auto means the model decides whether to call tools.
required means the model must call one or more tools.
none means the model does not call tools.
Therefore:
A . auto = Incorrect, because the model may or may not call the Azure function.
B . none = Incorrect, because this prevents tool/function calls.
C . required = Correct, because it forces the agent to call a tool.
The Azure OpenAI function-calling documentation also confirms that tool_choice="auto" lets the model decide whether to call a function, while tool_choice="none" forces a user-facing response without a tool call.
NEW QUESTION # 129
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