Quick Answer
The answer is sensitive information types, trainable classifiers, and data classification rules. Sensitive information types (SITs) are predefined or custom pattern-based detectors for common data like credit card numbers or passport IDs, while trainable classifiers use machine learning to identify nuanced content based on context and sample training, catching sensitive data that rigid patterns might miss. Data classification rules tie these together by applying conditions—such as combining a SIT with a trainable classifier match—to automatically label and discover sensitive content across your Microsoft 365 estate. On the Microsoft Cybersecurity Architect exam, this trio tests your understanding of how Purview’s discovery engine layers pattern matching, ML-driven inference, and policy-driven labeling; a common trap is assuming only SITs are needed, forgetting that trainable classifiers excel at detecting custom or ambiguous data like internal project codes. Memory tip: think “Patterns, Learning, Rules” to recall the three discovery pillars.
This SC-100 practice question tests your understanding of design security operations, identity, and compliance capabilities. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
An organization uses Microsoft Purview to classify and protect sensitive data. Which THREE capabilities can be used to discover sensitive data? (Choose three.)
Trainable classifiers
Machine learning models to identify content.
Data loss prevention policies
Why wrong: Policies to prevent data loss, not discover.
Retention labels
Why wrong: Labels for retention, not discovery.
Data classification rules
Rules to classify data based on conditions.
Sensitive information types
Built-in or custom patterns to identify sensitive data.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Trainable classifiers
Trainable classifiers use machine learning to identify content based on patterns and context, not just exact matches. They can be trained on sample data to recognize custom sensitive information, such as specific contract clauses or internal project codes, enabling discovery of sensitive data that predefined sensitive information types might miss.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Trainable classifiers
Why this is correct
Machine learning models to identify content.
Related concept
Read the scenario before looking for a memorised answer.
Data loss prevention policies
Why it's wrong here
Policies to prevent data loss, not discover.
Retention labels
Why it's wrong here
Labels for retention, not discovery.
Data classification rules
Why this is correct
Rules to classify data based on conditions.
Related concept
Read the scenario before looking for a memorised answer.
Sensitive information types
Why this is correct
Built-in or custom patterns to identify sensitive data.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Microsoft often tests the distinction between discovery capabilities (which identify sensitive data) and enforcement or lifecycle management capabilities (which act on already-discovered data), causing candidates to mistakenly select DLP policies or retention labels as discovery tools.
Detailed technical explanation
Trainable classifiers in Microsoft Purview leverage a two-step process: first, they analyze seed content to build a model using deep learning algorithms, then they apply that model to content in SharePoint, OneDrive, and Exchange to assign a classification. Sensitive information types use predefined or custom regex patterns and keyword lists, with confidence levels based on proximity and count. Data classification rules combine these with conditions like sensitivity labels to automatically tag content, enabling discovery at scale across Microsoft 365 workloads.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Design security operations, identity, and compliance capabilities — This question tests Design security operations, identity, and compliance capabilities — Read the scenario before looking for a memorised answer..
The correct answer is: Trainable classifiers — Trainable classifiers use machine learning to identify content based on patterns and context, not just exact matches. They can be trained on sample data to recognize custom sensitive information, such as specific contract clauses or internal project codes, enabling discovery of sensitive data that predefined sensitive information types might miss.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
This SC-100 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 SC-100 exam.
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