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 department needs to automatically extract specific types of information from court documents, such as the names of plaintiffs and defendants, dates of hearings, and names of presiding judges. The department has a large set of unlabeled documents but does not have any manually tagged examples. Which Azure AI Language feature should they use?
⚠ Common exam trap
Candidates often confuse Key Phrase Extraction with Named Entity Recognition, assuming that extracting 'important phrases' is the same as extracting specific entity types, but NER targets predefined categories while key phrase extraction returns arbitrary multi-word terms.
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
✓
Named Entity Recognition (NER)
Named Entity Recognition (NER) is the correct choice because it is a pre-built Azure AI Language feature designed to automatically identify and extract specific categories of information—such as person names, dates, and organizations—from unstructured text without requiring any labeled training data. The legal department's need to extract plaintiffs, defendants, hearing dates, and judges aligns directly with NER's out-of-the-box capabilities for common entity types like Person, Date, and Organization.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Named Entity Recognition (NER)
Why this is correct
Named Entity Recognition (NER) is a pre-built Azure AI Language capability that identifies and categorizes named entities in text, such as people, organizations, dates, and quantities, without any labeled training data. It runs out-of-the-box and can extract these entity types directly from the department's documents, precisely matching the need to automatically pull specific entities. This makes NER the correct choice because it requires no custom model building or labeled examples.
- ✗
Custom text classification
Why it's wrong here
Custom text classification is a labeled-data-driven Azure AI Language feature that trains a model to assign a document or sentence to one or more predefined categories, such as 'contract' or 'invoice'. The legal department has no labels and wants to extract entities, not classify entire documents. Therefore this option fails because it addresses a different text analytics task and demands labeled examples that the user does not possess.
- ✗
Key phrase extraction
Why it's wrong here
Key phrase extraction identifies the most relevant phrases in a document, usually multi-word terms like 'confidentiality clause' or 'employment contract', but it does not assign semantic types such as person, date, or organization to those phrases. It might surface 'Mary Smith' as a key phrase yet never label her as a person, so it cannot satisfy the requirement to extract specific, typed entities automatically. Thus it is incorrect for this entity-extraction scenario.
- ✗
Translation
Why it's wrong here
Translation converts text from one language to another, for example from English to Spanish, while aiming to preserve meaning and tone. It does not analyze or extract any structured entities from the source text, so it cannot pick out people, dates, or organizations. Since the legal department's goal is entity extraction rather than language conversion, translation is completely irrelevant to the task.
Go deeper
Related to this question
Learn chapter
Types of AI Workloads
Key term
Training data
Training data is a set of examples used to teach a machine learning model how to make predictions or decisions.
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
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