Microsoft AI-102 Exam With Confidence Using Practice Dumps

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 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.

For consistent, deterministic answers about policy PDFs with minimal effort, use Azure AI Language – Question Answering (Custom Question Answering). You can ingest PDFs into a knowledge base and define/curate the exact answers. The service then returns the same answer for the same question every time (subject to your KB content and configuration), avoiding the variability of generative models and custom ML. Microsoft Azure+2

Microsoft References:

What is Custom Question Answering? (Language service). Microsoft Learn

Azure AI Language – Question Answering product page. Microsoft Azure

Azure AI Language overview (includes Question Answering feature)

a tabular form of rows and columns

Relational data is, by definition, data that adheres to the relational model.

In the relational model, data is organized into tables, which consist of rows (records or tuples) and columns (fields or attributes). This is known as the tabular form.

The other options are incorrect:

Unstructured data is the opposite of relational data.

A hierarchical folder structure is used for file organization, not the logical structure of relational data.

Comma-separated value (CSV) files can contain relational data, but the core definition of relational data is the tabular structure, not the file format itself.

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