AI-901 Dumps with Practice Exam Questions Answers

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: The Temperature parameter can be set before deploying a model. = No

temperature is an inference/request parameter used when calling or testing a deployed model. It controls randomness in generated responses. It is not a required setting for deploying the model itself.

Statement 2: During inference, the model name is used to route requests to a specific deployment. = No

In Azure OpenAI / Microsoft Foundry deployments, application requests are routed to a specific deployment name, even when the SDK parameter is called model. The underlying model name, such as gpt-4.1-mini, is not what routes the request to the deployment.

Statement 3: After a model is deployed, both code and testing tools can be used to interact with the model. = Yes

After deployment, you can test the model in Foundry playground/testing tools or call the deployment from application code by using the endpoint, deployment name, and authentication credentials.

AudioOutputConfig(filename= " output.wav " )

The question specifically states the application must save the synthesized audio to a file. In the Azure Speech SDK for Python, speechsdk.audio.AudioOutputConfig(filename= " output.wav " ) directs the synthesizer to write the generated speech output directly to a WAV file on disk — which is exactly the requirement.

Why the other options are wrong:

AudioOutputConfig(stream) — This routes audio output to an in-memory audio stream object, not a file. It is used when you want to process or play the audio programmatically without saving it to disk.

AudioStreamFormat(wave_stream_format=AudioStreamWaveFormat.PCM) — This class defines the format of an audio stream (e.g., PCM encoding, sample rate). It is used when configuring custom audio streams, not when specifying a file output destination. It is not a valid argument for speechsdk.audio. in this context.

The correct and complete line is:

audio_config = speechsdk.audio.AudioOutputConfig(filename= " output.wav " )

The correct answer is API endpoint. After a generative AI model is deployed in Microsoft Foundry, the deployment exposes an inference endpoint through which external applications can submit requests and receive generated responses. Microsoft Foundry documentation describes deployed models as being accessible programmatically through an endpoint and associated credentials. Applications invoke the relevant REST API or SDK against this endpoint and specify the deployed model as required by the API.

An embedding vector is a numerical representation of semantic information used for similarity search, retrieval, and grounding; it is not the network interface through which a deployed model is invoked. A training dataset contains data used to train or fine-tune a model and is not called when performing inference. A URL parameter can be part of an API request, but it does not represent the deployed model ' s callable interface.

This directly aligns with the AI-901 objective to deploy a model and interact with it in the Foundry portal and to create lightweight applications that invoke deployed generative AI models through supported APIs and SDKs.

No

Yes

No

Comprehensive and Detailed Explanation with all Azure AI documents: =

Statement 1: Speech synthesis converts spoken audio into written text. = No

Speech synthesis, also called text to speech, converts written text into spoken audio. Converting spoken audio into written text is speech recognition or speech to text.

Statement 2: Speech recognition can detect and transcribe spoken input. = Yes

Speech recognition is used to identify spoken language in audio and convert it into text.

Statement 3: Text to speech in Azure Speech in Foundry Tools can translate text input into audible speech in a different language. = No

Text to speech converts text into spoken audio. Translation into another language is a separate translation capability, not standard text-to-speech synthesis.