AI-102 · Question #105
Drag and Drop Question You are building a Language Understanding model for purchasing tickets. You have the following utterance for an intent named PurchaseAndSendTickets. Purchase [2 audit…
The correct answer is GeographyV2; Email; Machine learned. The correct arrangement maps [Paris] to GeographyV2 (a built-in entity that recognizes cities, countries, and regions without requiring training data), [[email protected]] to Email (a built-in prebuilt entity that recognizes standard email address patterns without custom…
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Correct arrangement
- GeographyV2
- Machine learned
Explanation
The correct arrangement maps [Paris] to GeographyV2 (a built-in entity that recognizes cities, countries, and regions without requiring training data), [[email protected]] to Email (a built-in prebuilt entity that recognizes standard email address patterns without custom training), and [2 audit business] to Machine learned (because '2 audit business' is a complex, domain-specific ticket type/quantity combination that no built-in entity can recognize, requiring a custom machine-learned entity trained on examples). Using built-in entities (GeographyV2 and Email) minimizes the training data needed, while Machine learned handles the domain-specific label that has no suitable prebuilt equivalent. List and Regex are not optimal here: List requires manually enumerating all valid values (not minimal training effort for locations/emails), and Regex is pattern-based but unnecessary when built-in Email already handles email format recognition natively.
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