Describe features of Natural Language Processing workloads on Azure →mediumMultiple ChoiceObjective-mapped
AI-900 Practice Question: Describe features of Natural Language Processing workloads on Azure
A legal firm needs to automatically sort incoming legal documents into predefined categories such as 'Contract', 'Brief', 'Motion', and 'Discovery'. They have a set of 500 manually labeled documents to use as examples. Which Azure AI Language feature should they use to build this classification system?
⚠ Common exam trap
Many exam-takers confuse custom text classification with built-in features like key phrase extraction or named entity recognition, mistakenly thinking those can perform document-level categorization when they are designed for different NLP tasks.
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 the legal firm to train a model using their 500 labeled documents to classify text into predefined categories like 'Contract', 'Brief', 'Motion', and 'Discovery'. This feature enables supervised learning where the model learns from labeled examples to automatically sort incoming documents, which is exactly the requirement described.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Custom text classification
Why this is correct
Custom text classification is correct because the legal firm's task is to assign each incoming document to one of its own categories, such as contract, discovery, or brief. The Azure AI Language custom text classification feature lets you supply labeled examples, train a model on those user-defined categories, and then call the API to predict a category for new documents. This produces the whole-document class label required for automatic routing, which is exactly what the scenario needs.
- ✗
Key phrase extraction
Why it's wrong here
Key phrase extraction returns the most important words or short phrases in a document based on statistical and language-analysis scoring, but it does not output a category. For a legal firm, it might pull out terms like 'breach of contract' or 'settlement amount,' yet those phrases do not in themselves label the document as a particular matter type. Because it produces unlabeled surface content rather than a classification decision, it cannot be used to automatically assign incoming documents to predefined legal folders.
- ✗
Named entity recognition
Why it's wrong here
Named entity recognition locates and labels specific spans of text as predefined entities such as persons, organizations, dates, or monetary values. Even when using custom NER, it extracts entity mentions like 'Acme Corp' or 'January 15' but does not classify an entire document into a user-defined legal category. The sorting task requires a whole-document label, not a collection of recognized entities, so this option does not meet the legal firm's need.
- ✗
Sentiment analysis
Why it's wrong here
Sentiment analysis scores text for positive, negative, or neutral tone and can optionally include opinion-mining detail, but it is agnostic to topic or document type. A legal contract and a court filing could both receive a neutral sentiment score, yet they need to be routed to completely different practice areas. Because it measures emotional valence rather than content category, it cannot be used to sort incoming legal documents by kind.
Go deeper
Related to this question
Learn chapter
Machine Learning Core Concepts
Key term
Model
In IT and AI, a model is a trained mathematical representation that learns patterns from data to make predictions or decisions.
Key term
Classification
Classification is a supervised machine learning technique used to predict a category or class label for new data based on patterns learned from labeled training data.
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