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 legal firm needs to process thousands of contracts to automatically identify important terms such as dates, monetary amounts, names of parties, and legal citations. Which built-in feature of the Azure AI Language service is best suited for this task?
⚠ Common exam trap
Candidates often confuse Key Phrase Extraction with Entity Recognition, assuming both extract 'important terms' — but Key Phrase Extraction lacks the predefined, structured categorization needed for specific data types like dates and monetary amounts.
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
✓
C) Entity Recognition
Entity Recognition (also called Named Entity Recognition, NER) is the correct choice because it is specifically designed to identify and categorize predefined entities such as dates, monetary amounts, person names, organizations, and legal citations from unstructured text. The Azure AI Language service's NER capability can automatically extract these important terms from thousands of contracts, making it the ideal built-in feature for this task.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A) Sentiment Analysis
Why it's wrong here
Sentiment analysis returns a sentiment label and confidence scores for positive, negative, and neutral text, but it does not isolate individual facts such as contract parties, effective dates, or dollar amounts. For a legal contract workflow, the goal is structured data extraction, not opinion mining, so sentiment analysis would fail to produce the specific fields required. To extract those values, you need named entity recognition instead.
- ✗
B) Key Phrase Extraction
Why it's wrong here
Key Phrase Extraction surfaces the most salient topics or talking points in a document, but it returns unlabeled strings rather than categorizing them as recognizable entity types. While the phrase 'net 30 days' might appear as a key phrase, the service does not tag it as a duration or payment term, so downstream systems cannot reliably populate a contract database. Entity recognition, in contrast, labels each token with a semantic type.
- ✓
C) Entity Recognition
Why this is correct
Entity Recognition, specifically Azure AI Language's Named Entity Recognition (NER), identifies and categorizes entities in text into predefined types such as Date, Currency/Amount, Person, Organization, and Address. In a contract, it can extract the effective date as a DATE entity, the contract value as a Money entity, and the involved firms as Organization entities, making it the correct service. This enables automated downstream processing without manual review.
- ✗
D) Language Detection
Why it's wrong here
Language Detection identifies the dominant language of a document and returns a language code and score, but it provides no insight into the semantic content, parties, or financial terms. A contract written in English would simply be labeled 'en', which is irrelevant if the task is to pull out dates or monetary values. It is a preprocessing step, not an information extraction tool.
Go deeper
Related to this question
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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
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