Last Attempt AI-102 Questions

Scenario: All planned projects must support English, French, and Portuguese.

If a bot uses multiple LUIS models and QnA Maker knowledge bases (knowledge bases), you can use the Dispatch tool to determine which LUIS model or QnA Maker knowledge base best matches the user input. The dispatch tool does this by creating a single LUIS app to route user input to the correct model.

The requirements state:

All AI solution responses must have a confidence score ≥ 70%.

If the response confidence score is < 70%, the response must be improved by human input.

Members of the Consultant-Bookkeeper group must be able to process financial documents, which includes performing manual review when the AI confidence is below threshold.

Looking at the provided JSON:

" fields " : {

" ReceiptType " : {

" type " : " string " ,

" valueString " : " Itemized " ,

" confidence " : 0.672

},

" MerchantName " : {

" type " : " string " ,

" valueString " : " Tailwind " ,

" confidence " : 0.913

}

}

ReceiptType.confidence = 0.672 → this is below 0.7.

MerchantName.confidence = 0.913 → this is above 0.7.

Therefore, the correct condition for triggering manual review is:

documentResults.fields. < field > .confidence < 0.7

Now, evaluating the options:

A. documentResults.docType == " prebuilt:receipt "

Always true for receipts, does not check confidence. Not correct.

B. documentResults.fields.*.confidence < 0.7

This is the correct general expression: trigger manual review whenever any field confidence is below 0.7.

C. documentResults.fields.ReceiptType.confidence > 0.7

This would bypass manual review when ReceiptType has high confidence. The requirement is to trigger review when confidence < 0.7, so this is the opposite.

D. documentResults.fields.MerchantName.confidence < 0.7

This only checks one field (MerchantName). In the JSON, MerchantName has confidence 0.913 ( > 0.7), so this condition would not trigger, but ReceiptType clearly needs review. Too narrow.

Correct Answer: B. documentResults.fields.*.confidence < 0.7

Microsoft References

Azure AI Document Intelligence – Confidence scores

Human-in-the-loop for Document Intelligence

This scenario is about improving the accuracy of QnA Maker responses when user phrasing is different but semantically the same.

Problem

Uploaded document contains: “What is your warranty period?”

Users ask: “How long is the warranty coverage?”

QnA Maker fails because it does not automatically equate these two phrasings.

Both must map to the same answer.

Correct Sequence of Actions

Add alternative phrasing to the question and answer (QnA) pair.

In QnA Maker, you can add synonyms or paraphrases for a question.

Example: Add “How long is the warranty coverage?” as an alternative phrasing to the existing QnA pair.

This ensures the system treats both phrasings as the same question.

Retrain the model.

After modifying the knowledge base with new alternatives, you must retrain the model.

This step incorporates the updates into the QnA Maker service.

Republish the model.

To make the changes live and accessible by the chatbot endpoint, the retrained model must be republished.

Without republishing, the chatbot will still use the old knowledge base.

Actions That Are Not Required

Add a new QnA pair → Not needed, because we want both questions to map to the same existing answer, not duplicate answers.

Add additional questions to the document → That’s not required when QnA Maker already supports adding alternative phrasing within the portal or JSON editor.

Final Answer (Sequence)

Add alternative phrasing to the question and answer (QnA) pair.

Retrain the model.

Republish the model.

Microsoft References

QnA Maker – Add alternative phrasing

Train and publish a QnA Maker knowledge base