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 governs the character offsets reported for extracted entities, not whether an entity is detected at all. Changing it to Utf16CodeUnit corrects misaligned span positions when parsing responses programmatically, but it cannot cause a genuinely present 'Date' mention to be recognised.
- ✗
The API version does not support entity extraction
Why it's wrong here
API versioning affects available features and request schema, but the service still extracts every entity defined and labelled in the model. A missing Date entity points to the utterance lacking a labelled Date example or the model not recognising it, not to the API version's capabilities.
- ✗
The endpoint is pointing to the wrong deployment
Why it's wrong here
A wrong deployment endpoint would return errors or an entirely different model's predictions, not a correct intent with two of three entities. Redeploying is the fix when requests hit a stale or mismatched model version, but here the model resolved 'Seattle' and 'Boston' correctly, so the deployment is functioning.
- ✓
The model was not trained to recognize date entities
Why this is correct
Entity extraction depends entirely on labelled training utterances. If the model was never trained with examples containing date entities, the runtime cannot recognise them, so 'Date' is omitted even though intent and city entities resolve correctly.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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