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 extract case-specific entities such as 'docket number', 'plaintiff attorney', and 'court name' from legal documents. They have a small set of manually labeled examples for each entity. Which Azure AI Language feature should they use to build this custom entity extraction solution?
⚠ Common exam trap
Watch out — candidates often confuse 'prebuilt entity extraction' (which is fixed and generic) with 'custom named entity recognition' (which is trainable), assuming that prebuilt models can be adapted to domain-specific entities without additional training.
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 named entity recognition (NER)
Custom named entity recognition (NER) allows you to train a model with your own labeled examples to extract domain-specific entities like 'docket number' and 'plaintiff attorney'. Prebuilt entity extraction only recognizes common, generic entities (e.g., person, location) and cannot be customized for legal case-specific terms. This makes custom NER the correct choice for building a tailored extraction solution with a small set of manually labeled data.
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 named entity recognition (NER)
Why this is correct
Custom named entity recognition (NER) in Azure AI Language lets you define your own entity schema and train a model on labeled legal documents, so the model learns to extract case-specific fields such as docket numbers, case citations, and party roles. Because the legal firm has a specific extraction need, this per-case customization directly addresses it.
- ✗
Prebuilt entity extraction
Why it's wrong here
Prebuilt entity extraction in Azure AI Language recognizes widely applicable entity types like Person, Organization, Location, DateTime, and Numbers, but these are generic and cannot be retrained or extended. A prebuilt model would not identify a docket number as a docket number, nor would it understand the role of a 'judge' unless explicitly mapped to Person, making it insufficient for case-specific legal extraction.
- ✗
Key phrase extraction
Why it's wrong here
Key phrase extraction analyzes text to produce a list of significant terms or short phrases that represent the main topics, with no predefined entity labels attached. This means it cannot output structured fields such as 'case_id' or 'claim_amount' and does not distinguish between a person, a location, or a legal concept, so it is not a solution for extracting defined legal entities.
- ✗
Sentiment analysis
Why it's wrong here
Sentiment analysis evaluates whether text expresses positive, negative, or neutral opinion at the sentence or document level, returning confidence scores such as 0.8 positive. It has no concept of extracting individualized tokens or assigning entity types, so it would never return a case number or party name; it only summarizes overall tone, which is irrelevant to legal entity extraction.
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Azure Machine Learning Studio
Key term
Named entity recognition
Named entity recognition (NER) is an Azure AI service feature that automatically identifies and classifies key pieces of information in text, such as names of people, organizations, locations, dates, and other specific data.
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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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.