AI-102 Practice Question: Implement natural language processing solutions
You are building an Azure AI Language solution that ingests support tickets and routes each ticket to the correct department. Each ticket must be assigned exactly one department, and departments are defined only by the examples you label in Language Studio. You need to build the model with the fewest labeling and configuration steps. Which project type and configuration should you use?
⚠ Common exam trap
The trap here is assuming that more expressive project types such as multi-label classification or entity recognition are always better, when the routing requirement of exactly one department per ticket makes single-label classification the correct and simplest choice.
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
✓
A custom single-label classification project with one class per department
The scenario requires exactly one department per ticket and departments defined purely by labeled examples, which is precisely what custom single-label classification provides. Each department maps to one class, so the deployed model returns a single predicted department that can drive routing without additional logic. The other project types either permit multiple labels, extract text spans, or model utterances, none of which enforces a single whole-document category.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A custom multi-label classification project with one class per department
Why it's wrong here
Multi-label classification allows a document to receive multiple classes simultaneously. That does not match a routing scenario where exactly one department must own each ticket, and it introduces extra complexity in deciding which of several predicted labels becomes the routing target. It would also require the model to learn exclusivity that the single-label project enforces by design, adding unnecessary labeling and configuration effort.
- ✓
A custom single-label classification project with one class per department
Why this is correct
Single-label classification assigns exactly one class per document, matching the requirement that each ticket maps to one department. Each department becomes a class, so labeling a ticket with its department directly trains the model. This is the minimal configuration because no multi-label scoring, entity extraction, or orchestration layer is needed, and the deployed model returns the single predicted department for routing.
- ✗
A custom named entity recognition project with one entity type per department
Why it's wrong here
Named entity recognition extracts spans of text, such as product names or locations, rather than classifying the whole ticket. Using entity types per department would require each ticket to contain a literal mention of the department, which support tickets typically do not. It also returns zero or many entities per document, so it cannot guarantee the single department assignment the routing workflow requires.
- ✗
A conversational language understanding project with one intent per department
Why it's wrong here
Conversational language understanding predicts the intent of an utterance and extracts entities, which fits chatbots rather than document routing. Support tickets are multi-sentence documents, not short utterances, so utterance-based intent prediction is a poor fit. It also requires building utterances and entity labels, making it more configuration work than a single-label classification project that trains directly on whole tickets.
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Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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