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 law firm needs to automatically detect and redact sensitive information such as names, addresses, and social security numbers from legal documents. Which Azure AI Language feature can detect these entities without custom training?
⚠ Common exam trap
A common mix-up: candidates confuse Key Phrase Extraction with entity detection, but Key Phrase Extraction only returns general topics or concepts, not specific sensitive data like names or SSNs.
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
PII Detection is the correct Azure AI Language feature because it is specifically designed to identify and redact sensitive personal information such as names, addresses, and social security numbers from text without requiring any custom training. This pre-built capability uses machine learning models to detect categories of personally identifiable information (PII) out of the box, making it ideal for compliance scenarios like legal document processing.
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 the emotional tone of text, assigning positive, negative, neutral, or mixed sentiment scores at both document and sentence levels. It focuses on opinion and subjectivity, not on identifying entity types such as personal identifiers or contact details. Since the law firm needs to find and mask sensitive pieces of information rather than gauge the attitude expressed in a document, Sentiment Analysis is completely unrelated to the redaction task.
When this WOULD be correct
A company wants to automatically gauge customer satisfaction from support tickets by determining whether each ticket expresses positive, negative, or neutral sentiment. Sentiment Analysis would be the correct feature for this task.
- ✗
Key Phrase Extraction
Why it's wrong here
Key Phrase Extraction surfaces the most salient topics or terms in a document by ranking phrases based on their relevance to the overall content. While those extracted phrases might occasionally coincide with a person's name or address, the service does not categorize phrases according to predefined sensitive data types, nor does it guarantee that all PII is found. Consequently, relying on Key Phrase Extraction would lead to inconsistent omissions, making it unsuitable for a compliance-sensitive redaction workflow.
When this WOULD be correct
A company wants to automatically extract the main topics from customer feedback surveys to identify common themes. In this scenario, Key Phrase Extraction would be the correct Azure AI Language feature to use without custom training.
- ✓
PII Detection
Why this is correct
PII Detection is the correct choice because it is a specialized Azure AI Language capability that uses named entity recognition to locate and categorize sensitive data in unstructured text, such as names, social security numbers, email addresses, and physical addresses. It returns entity categories, confidence scores, and character offsets, which enable downstream processes to redact or mask the exact spans. This directly satisfies the law firm's requirement to automatically detect and redact sensitive information.
- ✗
Language Detection
Why it's wrong here
Language Detection identifies the predominant language of a document by returning a language name, ISO code, and confidence score, but it does not examine the semantic content for entities like names or social security numbers. It operates purely on linguistic characteristics for language identification, making it fundamentally incapable of locating any sensitive data that needs redaction. Therefore, even if the legal documents are in multiple languages, Language Detection would not help resolve the redaction requirement.
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 DetectionCorrect answer▾
Why this is correct
PII Detection is the correct choice because it is a specialized Azure AI Language capability that uses named entity recognition to locate and categorize sensitive data in unstructured text, such as names, social security numbers, email addresses, and physical addresses. It returns entity categories, confidence scores, and character offsets, which enable downstream processes to redact or mask the exact spans. This directly satisfies the law firm's requirement to automatically detect and redact sensitive information.
✗Sentiment AnalysisWrong answer — click to see why▾
Why this is wrong here
Sentiment Analysis detects emotional tone (positive, negative, neutral) in text, not sensitive entities like names or social security numbers. It cannot identify or redact PII.
★ When this WOULD be the correct answer
A company wants to automatically gauge customer satisfaction from support tickets by determining whether each ticket expresses positive, negative, or neutral sentiment. Sentiment Analysis would be the correct feature for this task.
Why candidates choose this
Candidates may confuse 'detecting sensitive information' with 'analyzing text content' and incorrectly assume sentiment analysis can identify entities like names or addresses.
✗Key Phrase ExtractionWrong answer — click to see why▾
Why this is wrong here
Key Phrase Extraction identifies important words or phrases in text, but it does not detect or redact sensitive entities like names, addresses, or social security numbers. The question specifically requires detection of PII, which is not supported by Key Phrase Extraction.
★ When this WOULD be the correct answer
A company wants to automatically extract the main topics from customer feedback surveys to identify common themes. In this scenario, Key Phrase Extraction would be the correct Azure AI Language feature to use without custom training.
Why candidates choose this
Candidates may confuse 'key phrases' with 'entities' or assume that extracting important phrases would include sensitive information, not realizing that PII detection is a separate, specialized feature.
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?”
Go deeper
Related to this question
Learn chapter
Machine Learning Core Concepts
Key term
Machine learning
Machine learning is a branch of artificial intelligence where computers learn patterns from data to make decisions or predictions without being explicitly programmed for every task.
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
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.