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NEW QUESTION # 35
What should you use to compare benchmark metrics of different AI models?
Answer: C
Explanation:
To compare benchmark metrics across Azure AI models, navigate to the Azure AI Foundry portal, explore the model catalog, and use the benchmarking tools to assess model performance. You can then compare these metrics to choose the most suitable model for your needs.
Reference:
https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/benchmark-model-in-catalog
NEW QUESTION # 36
You are developing an application that extracts structured information from different types of content by using Azure Content Understanding in Foundry Tools.
You need to extract scanned invoices in the PDF format and voicemail recordings in the WAV format.
Which type of analyzer should you use for each content type? To answer, drag the appropriate analyzer types to the correct content types. Each analyzer type may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 37
Your company has thousands of recorded customer support calls in multiple languages stored as audio files in Azure Storage.
You need to generate text transcripts of all the recordings.
Which Azure Speech in Foundry Tools capability should you use?
Answer: C
Explanation:
For thousands of recorded support calls stored as audio files in Azure Storage, the correct capability is speech to text batch transcription .
Microsoft states that batch transcription is designed to transcribe a large amount of audio data in storage , including audio files in Azure Blob Storage, and that files can be processed concurrently to reduce turnaround time.
Real-time transcription is for live audio, not large stored batches. Text to speech converts text into audio.
Speech translation translates speech between languages, but the requirement is to generate transcripts.
NEW QUESTION # 38
You are developing an application that converts text into spoken audio and saves the synthesized audio to a file by using Azure Speech in Foundry Tools.
How should you complete the Python code? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
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 " )
NEW QUESTION # 39
You have the following REST API request.
Which Azure OpenAI model should you use to process the request?
Answer: A
Explanation:
The REST API request contains an image-generation style payload:
{
" prompt " : " A polar bear on the beach holding an icecream " ,
" size " : " 1024x1024 " ,
" n " : 1,
" style " : " vivid "
}
This request asks the model to generate an image from a text prompt. In Azure OpenAI, DALL-E models are used for image generation from natural language prompts.
Why the other options are incorrect:
B). Codex is associated with code generation tasks.
C). GPT-4.1 is primarily used for text/chat and multimodal reasoning, not this classic image-generation API payload.
D). Whisper is used for speech-to-text transcription.
NEW QUESTION # 40
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