AI-102 Practice Question: Implement natural language processing solutions
Exhibit
{
"documents": [
{
"id": "1",
"entities": [
{
"text": "Seattle",
"type": "Location",
"subtype": null,
"offset": 14,
"length": 7,
"confidenceScore": 0.99
},
{
"text": "Microsoft",
"type": "Organization",
"subtype": null,
"offset": 30,
"length": 9,
"confidenceScore": 0.95
}
],
"warnings": []
}
],
"errors": []
}Refer to the exhibit. You called the Named Entity Recognition API on a document. Which entity type is "Seattle"?
⚠ Common exam trap
A common mix-up: candidates confuse the specific instance (e.g., 'City') with the official entity type label used by the API, leading them to choose 'City' instead of the correct 'Location' type.
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
✓
Location
The Named Entity Recognition (NER) API in Azure AI Language identifies 'Seattle' as a Location entity because it is a recognized geographical place. The API uses a pre-trained model that categorizes entities into types such as Location, Person, Organization, etc., and 'Seattle' falls under the Location type based on its semantic context in the document.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Organization
Why it's wrong here
Named Entity Recognition classifies Seattle as a Location, not an Organization, because the model distinguishes geographic entities from corporate or institutional names. It is tempting since organisations are frequently extracted entities, and that label would be correct for a company name such as Microsoft appearing in the same document.
- ✓
Location
Why this is correct
"Seattle" denotes a geographic place, so the Named Entity Recognition model classifies it under the Location entity category, which covers cities, countries and regions. This satisfies the stem's requirement to identify the entity type returned for a place name, rather than Person, Organisation or DateTime.
- ✗
Person
Why it's wrong here
"Seattle" is a Location entity, not a Person; NER classifies place names under Location/Geography. Person is tempting because it captures human names, which is the correct type when the extracted text is an individual's name such as "Satya Nadella".
- ✗
City
Why it's wrong here
The API's entity taxonomy uses Location for cities; City is not a returned category, so the label cannot be selected. It is tempting because Seattle is indeed a city, and a finer-grained geographic label would be correct if the model exposed city-level subtypes rather than the broader Location type.
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