AI-102 Custom text classification Practice Question
You are developing an Azure AI Language solution to analyze customer support tickets. Each ticket has a subject and a description. You need to automatically classify tickets into categories (e.g., 'billing', 'technical', 'account') and extract the product name mentioned. You have a labeled dataset of 10,000 tickets with category labels and product name annotations. The solution must be cost-effective and easy to retrain as new categories emerge. You want to use a single Azure AI Language resource. Which approach should you use?
⚠ Common exam trap
AI-102 often tests the choice between custom and prebuilt models; candidates may choose prebuilt NER for product names, not realizing that custom NER is needed for domain-specific entities.
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
✓
Use custom text classification for category and custom named entity recognition for product name extraction.
The most cost-effective and retrainable approach is to use custom text classification for categorizing tickets and custom named entity recognition (NER) for extracting product names. Both are part of Azure AI Language and can be trained on your labeled dataset. Custom NER allows you to define your own entity types (e.g., product name) and is more accurate than prebuilt NER for domain-specific products. Using a single Azure AI Language resource supports both features.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use custom text classification for category and key phrase extraction for product name.
Why it's wrong here
Using key phrase extraction for product names fails because it is a pre-trained capability that cannot leverage the provided 10,000 product name annotations to learn specific entities. It cannot be customised or retrained to extract new product names based on a custom dataset, which is a key requirement for a solution utilising labelled data. This approach is tempting because key phrase extraction offers a quick, cost-effective way to identify general important phrases without custom training, suitable for scenarios lacking specific entity labels or requiring only generic term identification.
- ✗
Use conversational language understanding (CLU) to handle both classification and entity extraction in a single model.
Why it's wrong here
Conversational Language Understanding (CLU) is designed for interpreting user *intents* and extracting entities from *utterances* within conversational applications, such as chatbots. While it offers both classification and extraction capabilities, it is not optimised for general *document classification* and *named entity recognition* on non-conversational text like support tickets. It would be the correct choice if the task involved understanding user queries in a dialogue system to drive conversational flow.
- ✓
Use custom text classification for category and custom named entity recognition for product name extraction.
Why this is correct
Both custom text classification and custom named entity recognition can be trained on the labeled dataset. They can be used within the same Azure AI Language resource, making the solution cost-effective and easy to retrain. This is the best approach.
- ✗
Use custom text classification for category and prebuilt named entity recognition for product name extraction.
Why it's wrong here
Custom text classification handles categorization well, but prebuilt named entity recognition cannot extract custom product names unless they happen to match prebuilt entity types (e.g., product names that are also organization names). Custom NER is needed for reliable extraction.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Microsoft exam blueprint
This AI-102 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-102 exam.