Implement natural language processing solutions →hardMultiple ChoiceObjective-mapped

AI-102 Practice Question: Implement natural language processing solutions

Exhibit

{
  "kind": "Conversation",
  "analysisInput": {
    "conversationItem": {
      "id": "1",
      "participantId": "user",
      "text": "I want to book a flight from Seattle to Boston next Tuesday"
    }
  },
  "parameters": {
    "projectName": "FlightBooking",
    "deploymentName": "production",
    "stringIndexType": "TextElement_V8"
  }
}

Refer to the exhibit. You send this request to the Conversational Language Understanding API. The response includes the intent 'BookFlight' with entities 'FromCity: Seattle' and 'ToCity: Boston', but the 'Date' entity is missing. What is the most likely cause?

⚠ Common exam trap

Test-takers frequently assume the API automatically extracts common entities like dates (similar to LUIS's prebuilt entities), but CLU requires all entities to be explicitly defined and trained in the model.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

✓

The model was not trained to recognize date entities

The Conversational Language Understanding (CLU) API returns only intents and entities that the deployed model was explicitly trained to recognize. If the training data did not include labeled 'Date' entities, the model will not extract them regardless of the input text. The API itself supports entity extraction, and the endpoint and string index type settings do not affect whether a specific entity type is recognized.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
The stringIndexType should be 'Utf16CodeUnit'
Why it's wrong here
StringIndexType affects how offsets are returned, not entity extraction.
✗
The API version does not support entity extraction
Why it's wrong here
All recent API versions support entity extraction.
✗
The endpoint is pointing to the wrong deployment
Why it's wrong here
The deployment is specified as 'production', which should be correct if it exists.
✓
The model was not trained to recognize date entities
Why this is correct
If the Date entity was not included in training, the model will not extract it.

Go deeper

AI-102 Practice Question: Implement natural language processing solutions

Exhibit

Why each option matters

Option-by-option breakdown

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