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AI-102 · Question #210

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might…

The correct answer is B. No. The chatbot fails to answer about product stock because a static PDF catalog typically lacks real-time inventory information, which question answering models cannot infer from the document alone.

Submitted by manish99· Mar 30, 2026Implement conversational AI solutions

Question

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution. After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen. You are building a chatbot that will use question answering in Azure Cognitive Service for Language. You have a PDF named Doc1.pdf that contains a product catalogue and a price list. You upload Doc1.pdf and train the model. During testing, users report that the chatbot responds correctly to the following question: What is the price of <product>? The chatbot fails to respond to the following question: How much does <product> cost? You need to ensure that the chatbot responds correctly to both questions. Solution: From Language Studio, you create an entity for cost, and then retrain and republish the model. Does this meet the goal?

Options

  • AYes
  • BNo

How the community answered

(61 responses)
  • A
    39% (24)
  • B
    61% (37)

Why each option

The chatbot fails to answer about product stock because a static PDF catalog typically lacks real-time inventory information, which question answering models cannot infer from the document alone.

AYes

The question 'Do you have <product> in stock?' requires information that is dynamic and external to a static PDF document. Simply training the model on the PDF will not enable it to answer questions about real-time stock availability, as that data is not present within the document itself.

BNoCorrect

The Azure Cognitive Service for Language's question answering feature extracts answers from the content of provided documents. A product catalogue and price list PDF typically contains descriptive and pricing information, but it does not usually include dynamic, real-time inventory stock levels. Therefore, the model cannot answer questions about stock availability from this source alone.

Concept tested: Limitations of document-based question answering for dynamic external data

Source: https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/overview

Topics

#Azure Custom Question Answering#Natural Language Understanding (NLU)#Chatbot troubleshooting#Language Studio entities

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