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 automatically process thousands of court documents. The system must identify and redact sensitive personal information such as names, addresses, and social security numbers. Additionally, it must extract legal-specific entities like case numbers, judge names, and statute references. The firm has a small set of manually annotated documents with these legal entities. Which combination of Azure AI Language features should they use?
⚠ Common exam trap
Test-takers frequently assume prebuilt entity recognition covers all entity types, but it lacks domain-specific entities, or they may think PII detection alone is sufficient, ignoring the need for custom extraction of legal 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
✓
PII detection and custom named entity recognition (NER)
The firm needs both PII detection to redact sensitive personal information and custom NER to extract legal-specific entities like case numbers and judge names from a small set of annotated documents. Azure AI Language provides a prebuilt PII detection feature for common sensitive data and a custom NER capability that can be trained on the firm's annotated documents to recognize domain-specific entities.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
PII detection and custom named entity recognition (NER)
Why this is correct
PII detection in Azure AI Language automatically identifies and protects sensitive personal data such as names, phone numbers, and addresses, which is essential for redaction. Custom named entity recognition (NER) goes further by training a model on annotated legal documents to extract domain-specific entities like case numbers, court names, and judge names. Using both together covers the firm's redaction requirements and the specialized extraction needs that prebuilt models cannot address.
- ✗
Prebuilt entity recognition and key phrase extraction
Why it's wrong here
Prebuilt entity recognition extracts common entities (e.g., people, places) but may not capture specialized legal entities like case numbers. Key phrase extraction identifies important phrases but does not provide structured entity extraction suitable for redaction or custom entity types.
When this WOULD be correct
A company needs to analyze customer feedback to identify common topics and extract key points from reviews, without needing custom entities or PII redaction.
- ✗
Sentiment analysis and language detection
Why it's wrong here
Sentiment analysis assigns a positive/negative/neutral score to text, while language detection simply identifies which language the document is written in. Neither operation extracts or redacts any text spans, and neither attempts to identify legal entities or personal data. Using these capabilities would leave all sensitive information and case-specific details untouched.
When this WOULD be correct
A company wants to analyze customer feedback from multilingual social media posts to determine overall sentiment and identify the languages used, without needing entity extraction or redaction.
- ✗
PII detection only
Why it's wrong here
PII detection alone only recognizes a fixed set of personal information types, such as SSNs, emails, and physical addresses. It cannot be taught to recognize legal-specific entities like case numbers, docket IDs, or judicial officer names, because those do not belong to any predefined PII category. The firm's extraction requirement therefore remains unmet without a custom trained model.
When this WOULD be correct
A question that asks only to identify and redact standard personal information (e.g., names, SSNs, credit card numbers) from documents, with no need for custom entity extraction. For example: 'A hospital needs to redact patient names and medical record numbers from clinical notes.'
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The AI-900 exam frequently reuses these exact scenarios with slightly different constraints.
✓PII detection and custom named entity recognition (NER)Correct answer▾
Why this is correct
PII detection in Azure AI Language automatically identifies and protects sensitive personal data such as names, phone numbers, and addresses, which is essential for redaction. Custom named entity recognition (NER) goes further by training a model on annotated legal documents to extract domain-specific entities like case numbers, court names, and judge names. Using both together covers the firm's redaction requirements and the specialized extraction needs that prebuilt models cannot address.
✗Prebuilt entity recognition and key phrase extractionWrong answer — click to see why▾
Why this is wrong here
Prebuilt entity recognition cannot identify legal-specific entities like case numbers or statute references, and key phrase extraction does not perform redaction or entity identification.
★ When this WOULD be the correct answer
A company needs to analyze customer feedback to identify common topics and extract key points from reviews, without needing custom entities or PII redaction.
Why candidates choose this
Candidates may think prebuilt entities cover all needed types and that key phrase extraction helps identify important information, overlooking the need for custom legal entities and PII redaction.
✗Sentiment analysis and language detectionWrong answer — click to see why▾
Why this is wrong here
Sentiment analysis and language detection do not identify or redact sensitive information or extract legal entities, which are the core requirements for processing court documents.
★ When this WOULD be the correct answer
A company wants to analyze customer feedback from multilingual social media posts to determine overall sentiment and identify the languages used, without needing entity extraction or redaction.
Why candidates choose this
Candidates may confuse general text analytics features with specialized entity recognition, or assume that any Azure AI Language feature can handle document processing tasks.
✗PII detection onlyWrong answer — click to see why▾
Why this is wrong here
The question requires extracting legal-specific entities like case numbers and judge names, which PII detection alone cannot handle. Custom NER is needed for those domain-specific entities.
★ When this WOULD be the correct answer
A question that asks only to identify and redact standard personal information (e.g., names, SSNs, credit card numbers) from documents, with no need for custom entity extraction. For example: 'A hospital needs to redact patient names and medical record numbers from clinical notes.'
Why candidates choose this
Candidates may think PII detection covers all sensitive information needs, overlooking the requirement for custom legal entities. They might also assume redaction is the only goal, ignoring the extraction of legal-specific terms.
Analysis generated from the official AI-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
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Related to this question
Learn chapter
Azure Machine Learning Studio
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.
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
This AI-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-900 exam.