Describe features of Natural Language Processing workloads on Azure →hardMultiple ChoiceObjective-mapped
AI-900 Practice Question: Describe features of Natural Language Processing workloads on Azure
A customer service organization has thousands of support tickets labeled with predefined categories such as 'Billing', 'Technical', and 'Account Management'. They want to build a solution that automatically assigns a category to new, incoming tickets. The categories are fixed and known in advance. Which Azure AI Language service feature should they use?
⚠ Common exam trap
Candidates often confuse Prebuilt Text Analytics (which offers out-of-the-box classification for sentiment or key phrases) with the need for custom classification, leading them to choose option A even though it cannot handle user-defined 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 (B) is correct because the organization has a fixed set of predefined categories and needs to classify new support tickets into those categories. This feature allows you to train a custom model using labeled examples of 'Billing', 'Technical', and 'Account Management' tickets, enabling automatic assignment of incoming tickets to the correct 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.
- ✗
Prebuilt Text Analytics
Why it's wrong here
Prebuilt Text Analytics is a set of out-of-the-box APIs in Azure AI Language for sentiment analysis, key phrase extraction, named entity recognition, and language detection. These abilities are governed by fixed, pre-trained models; you cannot supply your own labeled tickets to define a custom set of categories. Although it can identify entities like a product name or a date inside a ticket, it will not classify the entire ticket into your organization's specific support categories.
- ✓
Custom Text Classification
Why this is correct
Custom Text Classification is the correct choice because it is a feature of Azure AI Language designed specifically to train a machine learning model on your own labeled dataset. You provide support tickets annotated with your own custom categories, and the service learns patterns to assign those same labels to new tickets. It supports both single-label and multi-label classification, making it ideal for routing thousands of support tickets into predefined classes.
- ✗
Language Understanding (LUIS)
Why it's wrong here
Language Understanding (LUIS) is built for conversational AI, not document triage. It processes short user utterances to identify intents and extract entities, typically powering chatbots or voice assistants. Support tickets are long, descriptive documents, not conversational phrases, and they need to be assigned a ticket category rather than mapped to a conversational intent. Moreover, LUIS is deprecated in favor of Conversational Language Understanding, but even that is not the appropriate service for bulk document classification.
- ✗
Translator
Why it's wrong here
Translator is a neural machine translation service that converts text between languages while preserving meaning. It has no classification or categorization capability and cannot be trained with labeled data to recognize support ticket categories. Translating a ticket might help a human read it, but it does not route, tag, or classify the ticket in any way.
Go deeper
Related to this question
Learn chapter
Regression and Classification
Key term
Service
A service is a software component or system that performs a specific function and is available to be used by other programs or users over a network.
Key term
Model
In IT and AI, a model is a trained mathematical representation that learns patterns from data to make predictions or decisions.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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