Authentic Microsoft Certification Exam AI-901 Questions Answers

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 " )

To minimize unintended bias, the training data should be representative of the population for which the model will be used. Microsoft responsible AI guidance identifies fairness as a key principle and notes that discrepancies can occur when systems underperform for specific demographic groups or rarely observed input conditions in training data.

A global insurance dataset may not represent UK drivers. A completely random sample may still underrepresent important groups. Removing protected characteristics alone does not guarantee fairness, because other fields can act as proxies. The best answer is to use a training sample representative of the target UK population.

To automate masking of names and phone numbers in text data, use Personally Identifiable Information (PII) detection in Azure Language.

PII detection identifies sensitive personal information such as names, phone numbers, email addresses, addresses, and other personal identifiers. It can be used to detect and redact or mask that information from text.

The other options are incorrect:

A. custom text classification categorizes documents or text into labels.

C. entity linking identifies entities and links them to knowledge base entries.

D. custom named entity recognition (NER) can extract custom entities, but for built-in masking of names and phone numbers, PII detection is the correct feature.