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?
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.
Custom text classification can be trained on the labeled dataset to categorize tickets, and custom named entity recognition (NER) can be trained to extract product names from the text. Both services are part of the Azure AI Language resource, allowing a single resource to handle both tasks. This approach is cost-effective and easy to retrain as new categories emerge. Option A is incorrect because key phrase extraction is a prebuilt feature that returns general key phrases, not specifically trained to extract product names, and may miss them or include irrelevant phrases. Option B is incorrect because conversational language understanding (CLU) is optimized for multi-turn conversational flows and requires more complex configuration, making it less suitable for single-turn ticket classification and less cost-effective. Option D is incorrect because prebuilt NER only recognizes common entity types (e.g., person, organization) and cannot extract custom product names unless they match those predefined types.
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.
Go deeper
Related to this question
About these practice questions
This AI-102 question is part of Courseiva's 945-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.