AI-901 Exam Questions & Answers

For a vision-enabled model, Microsoft documentation shows that multimodal prompts can include both text and image content in the same user message content array. Azure OpenAI vision models accept multimodal image-and-text input and return natural language responses.

The correct request structure is a user message with a content array containing both items, for example:

{ 'role': 'user', 'content': [ {'type': 'input_text', 'text': 'What is in this image?'}, {'type': 'input_image', 'image_url': image_url} ] }

This avoids sending separate requests and is therefore the best choice for quickest response time.

A and C are incorrect because they send the text and image separately. B is incorrect because the image/question should be sent as a user message, while system messages are used for instructions and behavior.

The requirement is to extract structured fields, including nested values, from invoices by using a defined schema. In Azure Content Understanding, an analyzer is the processing unit that defines how content is analyzed, what information is extracted, and how the output is structured, including JSON fields.

Microsoft's Content Understanding document solutions documentation states that Content Understanding uses customizable analyzers to extract essential information, fields, and relationships from documents and forms. Microsoft's quickstart also shows invoice processing with the prebuilt-invoice analyzer to extract structured data from an invoice document.

Why the other options are incorrect:

A . transcription workflow in Azure Speech is for converting audio to text, not invoice field extraction. B . OCR-only document processing can extract text but does not meet the requirement for structured fields and nested values by schema. D . Azure AI Search is for indexing and querying content, not defining invoice extraction schemas.

Therefore, the correct answer is C. an analyzer in Azure Content Understanding in Foundry Tools.

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.

When using the Azure OpenAI Responses API with a vision-enabled model, the image must be included as an input image content item. The correct content item type is:

{'type': 'input_image', 'image_url': image_url}

Microsoft's Azure OpenAI Responses API documentation states that the Responses API supports image inputs, and multimodal requests use structured input content items for the request.

Why the other options are incorrect:

A . image_base64 = Incorrect. Base64 data can be used as the image data format, but the content item type is still input_image. B . image_generation = Incorrect. This is related to generating images, not providing an image for analysis. C . output_image = Incorrect. This would refer to generated output, not image input. D . input_image = Correct.

The requirement is to convert written customer notifications into natural-sounding spoken audio. This is speech synthesis, also known as text to speech.

Microsoft's Azure Speech documentation describes text to speech as a capability that converts text into natural-sounding synthesized speech. Therefore, for playing written notifications over a phone system, the correct Azure Speech capability is speech synthesis.

Why the other options are incorrect:

A . speaker recognition identifies or verifies speakers by voice. C . speech recognition converts spoken audio into text. D . speech translation translates spoken audio between languages.