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MS-102 Manage compliance by using Microsoft Purview Practice Question

A Microsoft Purview auto-labeling policy for sensitivity labels is matching too many SharePoint documents after simulation. Which two changes would most directly reduce false positives before enabling automatic labeling? (Choose two.)

⚠ Common exam trap

Many exam-takers think immediate enforcement (Option C) is the fastest way to fix false positives, but Microsoft explicitly recommends using simulation mode to tune rules before enabling automatic labeling, and waiting for user reports is not a valid tuning strategy.

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

✓

Increase the confidence level or instance-count requirement for the sensitive information type

Increasing the confidence level or instance-count requirement for the sensitive information type (SIT) directly reduces false positives by raising the threshold for what qualifies as a match. A higher confidence level means the classification engine requires stronger evidence (e.g., more keywords or a closer proximity to a pattern), while a higher instance count requires the sensitive data to appear multiple times in the document. Both adjustments make the auto-labeling rule more selective, ensuring only documents with a high likelihood of containing the specified sensitive content are labeled.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✓
Increase the confidence level or instance-count requirement for the sensitive information type
Why this is correct
Raising the confidence level or instance-count threshold in the sensitive information type (SIT) increases the probability that the detected pattern is a genuine match rather than an isolated or coincidental string. Confidence reflects the match strength of the classification engine, and instance count requires multiple occurrences in the same item, both of which reduce false positives in an auto-labeling policy.
✓
Add supporting keyword or contextual conditions to the auto-labeling rule
Why this is correct
Adding supporting keywords or contextual conditions to the auto-labeling rule requires the SIT to appear alongside corroborating evidence, such as an employee ID prefix, an address, or the word 'confidential.' This contextual refinement filters out unrelated numeric patterns that share the same format as the sensitive type, making the classification significantly more precise without sacrificing broad coverage.
✗
Turn on automatic labeling immediately and wait for users to report problems
Why it's wrong here
Enabling automatic labeling in enforcement mode immediately will apply labels to every matching item across your tenant at scale, which can amplify any misconfiguration into thousands of falsely classified files. Users will be presented with incorrect protection or governance actions, generating support tickets and requiring bulk remediation, instead of validating the rule first in simulation or test mode to fine-tune its thresholds.
✗
Replace the sensitivity label with a retention label
Why it's wrong here
Replacing the sensitivity label with a retention label changes the label's purpose entirely: sensitivity labels govern classification and protection (encryption, permissions, visual markings), while retention labels control content lifecycle such as deletion or retention after a defined period. The false positive problem stems from the SIT matching logic and rule conditions, so switching label categories neither improves detection accuracy nor addresses the underlying classification precision.

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MS-102 Manage compliance by using Microsoft Purview Practice Question

Why each option matters

Option-by-option breakdown

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