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 entities from contracts, such as 'Effective Date', 'Governing Law', and 'Payment Terms'. They have 500 manually labeled contract clauses that specify which text spans correspond to each entity. Which Azure AI Language feature should they use to build this custom entity extraction solution?
⚠ Common exam trap
Test-takers frequently confuse Prebuilt NER (which works out-of-the-box for common entities) with Custom NER (which requires labeled data but can extract any domain-specific entity), and they may incorrectly choose Prebuilt NER because they assume 'Effective Date' is a standard date entity, ignoring that the question explicitly requires custom entities like 'Governing Law' that are not predefined.
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 (Custom NER)
Custom Named Entity Recognition (Custom NER) is the correct choice because it allows you to train a model on your own labeled data (the 500 manually labeled contract clauses) to extract domain-specific entities like 'Effective Date', 'Governing Law', and 'Payment Terms' that are not covered by prebuilt models. This feature uses a custom trained model to identify and classify text spans according to your defined schema.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Prebuilt Named Entity Recognition (NER)
Why it's wrong here
Prebuilt Named Entity Recognition in Azure AI Language uses a fixed, pretrained model that assigns labels only from built-in categories such as Person, Organization, Location, and DateTime. Because this model cannot be retrained or extended on private legal data, it will not output custom entities like Governing Law, and the term 'Governing Law' in a contract may be ignored or mislabeled as a generic organization or location. Therefore it is unsuitable for extracting domain-specific legal fields without custom training.
- ✗
Key phrase extraction
Why it's wrong here
Key phrase extraction is an unsupervised extractive technique that returns the most relevant standalone words or short phrases from a document, ranked by their salience, but it does not associate those strings with any semantic type or data field. It has no concept of custom entity categories, nor can it be trained to recognize legal concepts such as Governing Law; it might return 'Governing Law' as a phrase, but without labeling it as a value for a field. Because key phrase extraction lacks the structured, typed output required for automated data capture, it is also a wrong answer.
- ✓
Custom Named Entity Recognition (Custom NER)
Why this is correct
Custom Named Entity Recognition (Custom NER) is a feature of Azure AI Language that lets you train your own model by tagging spans of text in labeled documents with entity types you define, such as GoverningLaw, EffectiveDate, or PartyName. After a model is trained and deployed, the prediction API extracts these exact spans and their associated types from new contracts, giving structured output suitable for downstream automation. Because it is fine-tuned on legal documents and supports arbitrary entity types, it directly satisfies the requirement to automatically extract specific legal clauses.
- ✗
Custom text classification
Why it's wrong here
Custom text classification builds a model that reads an entire document and assigns it a classification label, or multiple labels, based on the overall content—for example, 'Sales Contract' or 'NDA'. It does not locate or return the specific strings or token spans that justify the class, so it cannot be used to extract a value for 'Governing Law' from one sentence. For this reason, although it is a custom, trainable service, it produces document-level output, not entity-level extractions, so this option is wrong.
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Azure Machine Learning Studio
Key term
Entity extraction
Entity extraction is the process of automatically identifying and classifying named entities in text, such as people, organizations, locations, dates, and technical terms, turning unstructured data into structured information.
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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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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