In a bloom filter, which statement is true?

• A. It may report membership for an element that is not actually in the set (false positives).
• B. It guarantees exact membership testing with no false positives.
• C. It stores the exact set of elements in memory.
• D. It cannot be used for membership tests in security.

Answer: (A)

Bloom filters are probabilistic membership structures. They use a bit array and several hash functions to mark bits when you add elements. When you test for membership, you hash the item and check the corresponding bits; if any of those bits are 0, the item is definitely not in the set. If all are 1, you conclude it might be in the set—but it could be a false positive because those bits could have been set by other elements.

That’s why the statement about reporting membership for something not actually in the set is true: false positives can occur. It cannot guarantee exact membership testing because of this probabilistic nature. It also does not store the exact elements in memory; it stores only the bit array and the hash functions. And it can be used in security contexts for quick membership checks, so the idea that it cannot be used in security is not correct.