AI-102 Reviews Questions

For an Azure AI Language Custom Question Answering project (QA1), you can import Q & A pairs directly from Excel (.xls/.xlsx) or TSV files in Language Studio. Microsoft’s guidance explicitly states that when importing question–answer sources into a project, you can choose Excel or TSV. JSON, LU, and CSV are not supported as import file types for Q & A pairs in Language Studio. Microsoft Learn

Additional Microsoft guidance notes that structured TXT, TSV, and XLS files can be uploaded to create or augment a project, and clarifies import/export behavior for TSV and XLS. This reinforces that Excel and TSV are the supported formats here. Microsoft Learn

Microsoft References:

Move projects and Q & A pairs – “Import question and answers… option to import either an Excel or TSV file.” Microsoft Learn

Format guidelines for Custom Question Answering – structured TXT, TSV, and XLS supported; import/export notes for TSV and XLS. Microsoft Learn

For a phone-style bot that listens to customers and replies with spoken output, you use two core Azure AI Speech SDK objects:

SpeechRecognizer performs speech-to-text (STT), turning the caller’s audio into text that your app can use to query the orders database. This minimizes effort because it handles audio capture/streaming, language models, and returns recognized text events/results directly.

SpeechSynthesizer performs text-to-speech (TTS), converting the retrieved order status text into natural-sounding audio to play back to the customer. It supports neural voices and simple calls like SpeakTextAsync() .

Other options are not appropriate here:

TranslationRecognizer is for real-time speech translation, not needed for simple STT.

VoiceProfileClient is for speaker verification/identification, not for recognition or synthesis of the conversation content.

Microsoft References

Azure AI Speech SDK – Speech to text (SpeechRecognizer) overview and quickstarts.

Azure AI Speech SDK – Text to speech (SpeechSynthesizer) overview and quickstarts.

Azure AI Speech SDK concepts – recognition vs. synthesis pipelines.

Creating an entity for price is a feature of Conversational Language Understanding (CLU/LUIS), not Question Answering. Custom question answering does not use entities for extraction or intent resolution; it matches user utterances to Q/A pairs using alternates, synonyms, and ranking. Therefore, defining an “entity for price” would not make the chatbot correctly answer “How much does < product > cost?”. The correct fix is to author additional alternate phrasings (as in Q266).

References

Custom question answering capabilities (no entity modeling).

CLU entities vs. QnA authoring—separate features and workflows.