Setting the Similarity Threshold to zero reveals which aspect during testing?

Setting the Similarity Threshold to zero reveals which aspect during testing?

• A. The false negatives
• B. The false positives
• C. The overall accuracy
• D. The processing speed

Answer: (A)

Setting the Similarity Threshold to zero makes the detector extremely sensitive: any amount of similarity, even zero, is enough to trigger a match. In testing, this broadens what gets flagged so you can compare results against the known positives and see whether any sensitive data that should be detected actually slips through the cracks. If there are true positives that still aren’t flagged under this permissive rule, those are false negatives. This helps identify missed detections and verify that the system isn’t failing to catch sensitive data. The trade-off is that you’ll also flag many non-sensitive items (false positives), which would affect overall accuracy and can impact processing efficiency due to the higher alert volume, but the primary purpose here is to reveal any false negatives.

Setting the Similarity Threshold to zero makes the detector extremely sensitive: any amount of similarity, even zero, is enough to trigger a match. In testing, this broadens what gets flagged so you can compare results against the known positives and see whether any sensitive data that should be detected actually slips through the cracks. If there are true positives that still aren’t flagged under this permissive rule, those are false negatives. This helps identify missed detections and verify that the system isn’t failing to catch sensitive data. The trade-off is that you’ll also flag many non-sensitive items (false positives), which would affect overall accuracy and can impact processing efficiency due to the higher alert volume, but the primary purpose here is to reveal any false negatives.