MS-102 Manage compliance by using Microsoft Purview Practice Question
A compliance officer needs to automatically identify and label content that is conceptually similar to existing sensitive documents, such as internal strategy memos or proprietary technical specifications, without relying on explicit keywords or recognized sensitive information types. Which Microsoft Purview solution should the officer use to achieve this?
⚠ Common exam trap
Many exam-takers confuse trainable classifiers with sensitive information types, assuming that keyword or regex-based patterns are sufficient for conceptual similarity, when in fact trainable classifiers are the only Microsoft Purview solution that uses machine learning to identify content based on learned patterns rather than explicit rules.
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
✓
trainable classifier
A trainable classifier uses machine learning to identify content based on patterns and context learned from sample documents, making it ideal for recognizing conceptually similar content without relying on explicit keywords or predefined sensitive information types. This allows the compliance officer to automatically label internal strategy memos or proprietary technical specifications that share conceptual similarity with existing sensitive documents.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
trainable classifier
Why this is correct
Trainable classifiers are designed to identify content based on examples and can learn to recognize documents that are conceptually similar, such as internal memos or proprietary specs, without needing exact keywords or predefined sensitive info types.
- ✗
sensitive information type
Why it's wrong here
Sensitive information types (SITs) are pattern-based detectors that match structured data via regular expressions, keywords, and checksums—such as credit card numbers or social security numbers. They cannot generalize from example documents to recognize conceptually similar content like a proprietary memo or internal specification. A trainable classifier is needed for that semantic learning, so SITs would fail the compliance officer's requirement.
- ✗
An auto-labeling policy with a retention label
Why it's wrong here
Auto-labeling policies can apply retention labels based on sensitive info types or trainable classifiers, but the retention label itself is for retention/disposition, not for identification. The policy uses an underlying detection method, not the label.
- ✗
Data Loss Prevention (DLP) policy that blocks sharing
Why it's wrong here
A Data Loss Prevention policy that blocks sharing is an enforcement action triggered after content has already been classified by another method, such as a sensitive information type or trainable classifier. It does not perform the underlying identification of unclassified content; it simply applies a protective response. Therefore, selecting this as the identification mechanism confuses detection with remediation.
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Key term
Microsoft Purview
Microsoft Purview is a unified data governance and compliance service that helps organizations discover, manage, and protect their data across on-premises, cloud, and hybrid environments.
Key term
Compliance
Compliance is the process of ensuring that an organization follows laws, regulations, standards, and internal policies that apply to its operations and data handling.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MS-102 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 MS-102 exam.