Describe features of Natural Language Processing workloads on Azure →hardMultiple ChoiceObjective-mapped
Custom Text Classification for Ticket Categorization
A company wants to automatically categorize support tickets into categories such as 'Billing', 'Technical Issue', and 'Account Management'. They have a set of 1,000 labeled tickets that they can use to train a model. Which Azure AI Language feature should they use?
Quick Answer
The answer is custom text classification, because it is the Azure AI Language feature designed to train a model on your own labeled dataset—such as the 1,000 tickets here—to categorize text into user-defined classes like 'Billing', 'Technical Issue', and 'Account Management'. Unlike pre-built classification options that only recognize fixed categories, custom text classification learns from your specific examples, making it ideal for ticket categorization where the categories are unique to your business. On the AI-900 exam, this question tests your understanding of when to choose a custom solution versus a pre-built one; a common trap is selecting a pre-built sentiment or key phrase extraction feature, which cannot handle custom labels. Remember the memory tip: if you have your own labeled data and custom categories, think “custom” for custom text classification.
⚠ Common exam trap
Many candidates confuse custom text classification with pre-built features like key phrase extraction or sentiment analysis, assuming any NLP feature can categorize text without realizing custom training is required for specific categories.
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
✓
Custom text classification
Custom text classification is the correct choice because it allows you to train a model on your own labeled dataset (1,000 tickets) to categorize text into custom-defined classes like 'Billing', 'Technical Issue', and 'Account Management'. This feature is specifically designed for scenarios where you need to classify documents into user-defined categories, unlike pre-built classification options.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Key phrase extraction
Why it's wrong here
Key phrase extraction identifies important words and phrases but does not perform categorization into user-defined classes.
- ✓
Custom text classification
Why this is correct
Custom text classification is the correct feature because it allows training a model with labeled examples to assign tickets to custom categories like 'Billing' or 'Technical Issue'.
- ✗
Sentiment analysis
Why it's wrong here
Sentiment analysis evaluates the emotional tone of text (positive, negative, neutral) and is not designed to classify text into topic categories.
- ✗
Language detection
Why it's wrong here
Language detection identifies the language of the text (e.g., English, Spanish) and does not provide topic-based categorization.
Go deeper
Related to this question
Learn chapter
Regression and Classification
Key term
Azure AI Language
Azure AI Language is a cloud-based service from Microsoft that uses natural language processing to understand, analyze, and generate human language for applications.
Key term
Feature
A feature is a distinct unit of functionality that delivers value to the user, often managed and tracked throughout the software development lifecycle.
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Same concept, more angles
1 more way this is tested on AI-900
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A law firm wants to automatically categorize incoming legal documents into custom categories such as 'Motion', 'Contract', 'Discovery', and 'Memorandum'. The firm has a set of manually labeled documents that can be used to train the system. Which Azure AI Language feature should they use?
medium- A.Prebuilt Text Analytics for sentiment
- ✓ B.Custom text classification
- C.Conversational Language Understanding
- D.Key phrase extraction
Why B: The law firm needs to categorize documents into custom categories using their own labeled data. Custom text classification in Azure AI Language is specifically designed for this purpose, allowing you to train a model on your own labeled documents to classify text into user-defined categories. Prebuilt Text Analytics for sentiment only detects sentiment (positive/negative/neutral), not custom categories.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-900 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-900 exam.