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Microsoft AI-901 Exam Prep
Microsoft Azure AI Fundamentals (Page 2 )

Updated On: 12-Oct-2026
Page 2 of 27
PDF with 158 Questions

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?

  1. AudioOutputConfig
  2. SpeechSynthesizer
  3. AudioConfig
  4. SpeechConfig

Answer(s): D

Explanation:

To configure the connection to the Azure Speech service, you should use the azure.cognitiveservices.speech.SpeechConfig class.
Implementation Details Initialization: This class holds authentication and endpoint details, such as your Azure resource key and region.
Usage: You pass this configuration object into your real-time processing classes, like SpeechRecognizer (for speech-to-text) or SpeechSynthesizer (for text-to-speech).


Reference:

https://docs.azure.cn/en-us/ai-services/speech-service/how-to-recognize-speech



HOTSPOT (Drag and Drop is not supported)
Select the answer that correctly completes the sentence.
Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:



Box: a base64-encoded image data When using the OpenAI Responses API and a vision-enabled model, you can include an image in a request by providing the image as _______________.
When using the OpenAI Responses API with a vision-enabled model, you can include an image in a request by providing it in three different formats: A fully qualified image URL: Passing the web link where the image is hosted online. *-> A Base64-encoded data URL: Encoding a local image file directly into a text string (formatted as data:image/jpeg;base64,...). A file ID: Uploading the image via the Files API first and referencing its unique ID.


Reference:

https://developers.openai.com/api/docs/guides/image-generation



You need to create an AI agent in Microsoft Foundry that follows a specific role and behavior when responding to users.
What should you configure?

  1. system instructions
  2. temperature
  3. tokens per minute (TPM)
  4. max completion tokens

Answer(s): A

Explanation:

In Microsoft Foundry, instructions (formerly referred to as system messages) are the primary way to define the role, behavior, tone, and constraints of your AI agent.


Reference:

https://learn.microsoft.com/en-us/azure/foundry-classic/agents/quickstart



HOTSPOT (Drag and Drop is not supported)
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
Note: Each correct selection is worth one point.
Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:




Box 1: No No - System prompts can be used to authorize users.
In Azure AI, system prompts cannot securely authorize users. They guide model behavior, but because LLMs are susceptible to prompt injections and jailbreaks, relying on them for security poses severe vulnerabilities.Instead, you should handle authorization and authentication through secure infrastructural controls and middleware.
Box 2: Yes Yes - A system prompt used to reduce tokens per minute (TPM).
A system prompt can be specifically engineered to significantly reduce your Tokens Per Minute (TPM) consumption in Azure OpenAI Service. TPM limits are triggered by the combined volume of input (prompt) tokens and output (completion) tokens.
The primary mechanism of a "TPM-reduction system prompt" is to strictly constrain the model’s output verbosity and eliminate conversational filler, which prevents your application from hitting 429 TooManyRequests throttling errors.
Box 3: Yes Yes - A system prompt guides the behavior of a generate AI model.
In Azure AI (and generative AI in general), a system prompt (often called a system message or metaprompt)
acts as the foundational instruction set that dictates the AI model's behavior, tone, operational constraints, and guardrails.A system prompt serves as a "foundational job description" for the model. It sits in the backgro


Reference:

https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/system-message



You are developing a web app that processes invoices to calculate expenses.
You need to extract structured fields, including nested values, from the invoices by using a defined schema.
What should you use?

  1. an optical character recognition (OCR)-only document processing pipeline
  2. a transcription workflow in Azure Speech in Foundry Tools
  3. an analyzer in Azure Content Understanding in Foundry Tools
  4. an Azure AI Search service

Answer(s): C

Explanation:

An analyzer in Azure Content Understanding within Foundry Tools can be used for this scenario.
Prebuilt Invoice Support: It offers prebuilt invoice analyzer templates specifically trained to identify common transactional data out of the box.
Schema-Driven Extraction: Instead of manual prompt engineering or tedious labeling, you can supply a strict JSON schema containing the specific fields you need.
Nested Values & Layouts: It handles hierarchical, nested elements (such as itemized tables, nested line items, or customer details) and maps them cleanly into arrays or structured objects.
Web App Integration: Activating the analyzer creates a highly scalable REST API endpoint. Your invoice-processing web app can query this endpoint to receive clean, machine-readable structured JSON payloads ready for downstream expense calculation.


Reference:

https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/overview



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