The Microsoft responsible AI principle of transparency requires that AI systems be explainable to users .

Transparency means users should understand when they are interacting with AI, what the system can and cannot do, and how outputs or predictions are generated at an appropriate level.

The other options map to different responsible AI principles:

protect sensitive user data = Privacy and security

reduce bias in decisions = Fairness

require human oversight = Accountability

“AI systems must be understandable” is an example of the Microsoft responsible AI principle of transparency . Microsoft’s responsible AI framework includes fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability.

The other options are incorrect: AI systems should not merely protect company interests, should not be limited only to public data, and must not keep personal details public.

Speech recognition is used to recognize spoken language and convert it into written text. Therefore, the correct match is:

Speech recognition = Transcribes spoken input

Speech synthesis , also known as text to speech , is used to convert text into spoken audio. Therefore, the correct match is:

Speech synthesis = Generates spoken output

The other options are incorrect for these two tools:

Extracts entities from the output is a text analytics / language processing task.

Translates spoken input is speech translation.

Translates written output is text translation.

Azure Content Understanding analyzers define the type of content to process, such as documents, images, audio, or video , and what to extract, such as text, layout, fields, transcripts, and structured JSON output. Microsoft lists base analyzer types such as prebuilt-document, prebuilt-audio, prebuilt-video, and prebuilt-image.

For scanned invoices in PDF format , the correct choice is document analyzer , because invoices are document/form content. Microsoft’s Content Understanding quickstart specifically uses the prebuilt-invoice analyzer to extract structured data from an invoice PDF.

For voicemail recordings in WAV format , the correct choice is audio analyzer , because voicemail is audio content. Content Understanding supports structured data extraction from audio files, and the analyzer reference identifies prebuilt-audio as the base analyzer for audio content.

For a local JPEG image in a Python application using the Azure OpenAI Responses API, the image should be read, base64 encoded, and sent as a data URL, such as:

{ " type " : " input_image " , " image_url " : " data:image/jpeg;base64, < base64_image_data > " }

Microsoft’s Responses API documentation shows vision-enabled requests using input_image with image_url set to a base64 data URL in the format data:image/jpeg;base64,{base64_image}. It also confirms that JPEG images are supported.

A local file path such as file:///C:/images/photo.jpg or C:\images\photo.jpg is not a valid API-accessible image input. A public or accessible HTTPS URL can be used, but the question specifically says the application sends a local JPEG image , so the correct choice is the base64 data URL.

input=[

{

" role " : " user " ,

" content " : [

{ " type " : " input_text " , " text " : " What is in this image? Provide 3 bullet points. " },

{ " type " : " input_image " , " image_url " : image_url},

],

}

]

print(response.output_text)

For a multimodal request that sends both text and an image to an Azure OpenAI model, the input content array uses separate content item types. Text content is sent with type: " input_text " , and image content is sent with type: " input_image " . Microsoft’s Azure OpenAI Responses API documentation shows the Responses API supports multimodal inputs and uses structured input content items in the request.

The options output_text and output_image are not used inside the user input content array. In the shown code, response.output_text is used after the response is returned to print the model’s text answer.

Statement 1: Azure Content Understanding in Foundry Tools can analyze only PDF documents. = No

Azure Content Understanding is not limited to PDF documents. It can analyze different content types, including documents, forms, images, audio, and video.

Statement 2: Azure Content Understanding in Foundry Tools results are returned in the JSON format. = Yes

Azure Content Understanding returns structured analysis output in JSON format.

Statement 3: Azure Content Understanding in Foundry Tools can extract structured fields from documents and forms. = Yes

Azure Content Understanding can extract structured fields from documents and forms by using analyzers and schemas.