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 key information from contracts, including the names of parties involved, important dates, and monetary amounts. Which Azure AI Language feature should they use to identify and extract these specific pieces of information from the text?
⚠ Common exam trap
Candidates often confuse key phrase extraction with named entity recognition, but key phrase extraction does not categorize phrases into specific entity types like dates or monetary amounts, which is the core requirement in this question.
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 Azure AI Language feature because it is specifically designed to identify and categorize entities such as people (parties involved), dates, and monetary amounts from unstructured text. This directly matches the legal firm's requirement to extract key information from contracts.
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 evaluates text to assign an emotional polarity score, such as positive, negative, or neutral, typically at the sentence or document level. Its goal is to gauge opinion or emotion, not to identify or extract specific pieces of information like a person's name, a contract date, or a monetary amount. Consequently, while it may show whether a clause is favorable, it returns no structured entities and thus cannot meet the firm's requirement to automatically pull key facts from legal documents.
- ✗
Key phrase extraction
Why it's wrong here
Key phrase extraction scans text and returns a ranked list of the most salient terms or multi-word expressions, often without assigning them to any semantic category. These phrases are selected based on statistical significance or topic relevance, but the service does not classify them as, for example, Person, Date, or Money, nor does it resolve them into structured records. Therefore, although it may surface phrases like 'the plaintiff' or 'March settlement', it does not reliably extract named entities in the predefined, type-tagged manner the legal firm needs.
- ✓
Named Entity Recognition (NER)
Why this is correct
Named Entity Recognition (NER) identifies spans of text that reference predefined entity types, including Person, Organization, Location, Date, Quantity, Money, and others, and labels each span accordingly. This directly addresses the firm's goal because it automatically extracts key facts such as names of clients, court dates, and settlement amounts in a structured, type-coded format. Azure's NER in the Language service also supports custom entities, making it particularly adaptable to legal terminology and case-specific information.
- ✗
Language detection
Why it's wrong here
Language detection is a text classification task that examines the lexical and syntactic patterns of a document to determine which human language it is written in, returning a language code such as 'en' or 'es' with a confidence score. It does not parse the content for meaningful details, so it yields no entities, dates, or amounts. For the legal firm's use case, language detection would only be useful as a preprocessing step to route documents, never as the extraction mechanism itself.
Go deeper
Related to this question
Learn chapter
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
Azure AI Language
Azure AI Language is a cloud-based service from Microsoft that uses natural language processing to understand, analyze, and generate human language for applications.
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