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 analyze thousands of contracts to extract key information such as party names, dates, and monetary amounts. They also need to identify if certain clauses (e.g., non-compete, termination rights) are present. Which combination of Azure AI Language features should they use?
⚠ Common exam trap
Many candidates confuse Key Phrase Extraction with Entity Linking or Sentiment Analysis, mistakenly thinking that identifying clauses requires understanding sentiment or linking to external knowledge, rather than recognizing that Key Phrase Extraction directly surfaces the key topics and clauses present in the text.
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 and Key Phrase Extraction
Named Entity Recognition (NER) extracts predefined entities such as person names, dates, and monetary amounts from text, which directly addresses the need to identify party names, dates, and monetary amounts in contracts. Key Phrase Extraction identifies the main points or topics in a document, making it suitable for detecting the presence of specific clauses like non-compete or termination rights by surfacing the key phrases that represent those clauses.
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 and Key Phrase Extraction
Why this is correct
Named Entity Recognition (NER) in Azure AI Language extracts structured entities such as person names, organizations, dates, and currency amounts from contract text, while Key Phrase Extraction identifies the most salient terms and phrases, enabling the discovery of clause topics like 'termination' or 'liability limitation.' Together, these pre-trained capabilities convert unstructured legal documents into searchable metadata without requiring custom model training. This combination directly targets the legal firm's need to pull out both discrete data points and important contract language.
- ✗
Entity Linking and Language Detection
Why it's wrong here
Entity Linking disambiguates mentions by connecting them to a knowledge base such as Wikipedia, so it resolves ambiguous names but does not tag contract-specific fields like dates or monetary amounts. Language Detection only answers 'what language is this text written in?' and offers zero extraction ability. While these capabilities are valuable elsewhere, neither contributes to identifying clauses, dates, names, or amounts in legal contracts.
- ✗
Sentiment Analysis and Key Phrase Extraction
Why it's wrong here
Sentiment Analysis scores text on a positive/negative/neutral scale, which is distinctly unhelpful when contracts require objective factual extraction—contracts may be neutral in tone yet have critical dates and amounts. Key Phrase Extraction alone does provide some useful phrases, but it lacks the structured entity typing that NER gives, so it won't label 'June 30, 2025' as a date or '$100,000' as a monetary value. Therefore, the combination of sentiment with key phrases misses the core extraction needs.
- ✗
Text Analytics for Health and Entity Recognition
Why it's wrong here
Text Analytics for Health is a specialized pre-built model tuned for biomedical entities like symptoms, medications, and diagnoses, so running it on legal contracts would yield mostly irrelevant labels and miss legal concepts. The generic 'Entity Recognition' capability is NER, but pairing it with a healthcare-focused model does not adapt it for legal terminology. There is no legal-specific pre-built model in Azure AI Language, so this option would fail to extract obligation dates, parties' roles, or monetary terms effectively.
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Azure Machine Learning Studio
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.
Key term
Key phrase extraction
Key phrase extraction is an Azure AI service feature that automatically identifies and extracts the most important words and phrases from a piece of text.
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