AI-901 Certification Exam Guide + Practice Questions Updated 2026

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")

Explanation:
The Microsoft responsible AI principle of inclusiveness means AI systems should be designed to empower and engage everyone, including people with different abilities, languages, and accessibility needs.
Therefore, designing the interface to support multiple languages and screen readers is an example of inclusiveness.
Why the other options are incorrect:
A. Provide explanations about how predictions are generated = Transparency
C. Evaluate model outputs across demographic groups to reduce bias = Fairness
D. Encrypt stored customer data and restrict access by using role-based controls = Privacy and security

Explanation:
A schema defines which fields to extract when analyzing content.
In Azure Content Understanding, the schema or field Schema defines the structured data that the analyzer extracts from content, including field names, types, and extraction behavior. Microsoft documentation states that Content Understanding lets you define a schema to extract, classify, or generate field values from unstructured content.
The other options are incorrect:
A keyword list does not define the complete structured output fields.
OCR-only processing extracts text, but it does not define structured fields.
A synchronous API call describes a request pattern, not the extraction schema.

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.

Explanation:
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.