Describe features of Natural Language Processing workloads on Azure →mediumMultiple SelectObjective-mapped
AI-900 Practice Question: Describe features of Natural Language Processing workloads on Azure
A law firm needs to automatically process incoming legal documents. They have two specific requirements: (1) extract the names of all parties involved, the court name, and the filing date; (2) categorize each document as a 'complaint', 'motion', or 'subpoena'. Which two Azure AI Language features should they use? (Choose two.)
⚠ Common exam trap
A common mix-up: candidates confuse key phrase extraction with named entity recognition, but key phrase extraction returns untyped phrases rather than structured entities with predefined categories, and it cannot perform document-level classification.
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 (C) is correct because it allows the law firm to train a model to categorize legal documents into custom classes like 'complaint', 'motion', or 'subpoena' based on labeled examples. This feature is designed for domain-specific classification tasks where predefined categories are insufficient.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Sentiment analysis
Why it's wrong here
Sentiment analysis determines the emotional tone of text, not entity extraction or document categorization.
- ✗
Key phrase extraction
Why it's wrong here
Key phrase extraction identifies important phrases but does not extract specific structured entities or classify documents into custom categories.
- ✓
Custom text classification
Why this is correct
Custom text classification can be trained to assign user-defined labels such as 'complaint', 'motion', or 'subpoena' to documents.
- ✓
Named entity recognition (NER)
Why this is correct
NER extracts specific entities like names of parties, court names, and dates from unstructured text, fulfilling the extraction requirement.
Go deeper
Related to this question
Learn chapter
Regression and Classification
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.
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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