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Microsoft

AI-102 · Question #106

Drag and Drop Question You have a chatbot that uses a QnA Maker application. You enable active learning for the knowledge base used by the QnA Maker application. You need to integrate user input…

The correct answer is For the knowledge base, select Show active learning suggestions.; Approve and reject suggestions.; Save and train the knowledge base.; Publish the knowledge base. The correct sequence for integrating active learning suggestions into a QnA Maker knowledge base follows a logical review-and-retrain workflow: (1) 'For the knowledge base, select Show active learning suggestions' to view what the system has collected from user queries, (2)…

Submitted by fatima_kr· Mar 30, 2026Implement natural language processing solutions using Azure Cognitive Services, specifically managing and improving QnA Maker knowledge bases through active learning integration.

Question

Drag and Drop Question You have a chatbot that uses a QnA Maker application. You enable active learning for the knowledge base used by the QnA Maker application. You need to integrate user input into the model. Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order. Answer:

Exhibit

AI-102 question #106 exhibit

Answer Area

Drag items

Add a task to the Azure resource.Approve and reject suggestions.Publish the knowledge base.Modify the automation task logic app to run an Azure Resource Manager template that creates the Azure Cognitive Services resource.For the knowledge base, select Show active learning suggestions.Save and train the knowledge base.Select the properties of the Azure Cognitive Services resource.

Correct arrangement

  • For the knowledge base, select Show active learning suggestions.
  • Approve and reject suggestions.
  • Save and train the knowledge base.
  • Publish the knowledge base.

Explanation

The correct sequence for integrating active learning suggestions into a QnA Maker knowledge base follows a logical review-and-retrain workflow: (1) 'For the knowledge base, select Show active learning suggestions' to view what the system has collected from user queries, (2) 'Approve and reject suggestions' to curate which alternate questions are valid, (3) 'Save and train the knowledge base' to incorporate the approved suggestions into the model, and (4) 'Publish the knowledge base' to deploy the updated model so the chatbot uses the improved version. This sequence ensures that user feedback is reviewed, validated, trained, and then deployed in the correct order.

Topics

#QnA Maker#Active Learning#Knowledge Base Management#Azure AI Services

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