Count Prefix and Suffix Pairs II
A hard-tier problem at 27% community acceptance, tagged with Array, String, Trie. Reported in interviews at Autodesk and 3 others.
Count Prefix and Suffix Pairs II is a hard problem that shows up in assessments at Autodesk, Samsung, Capital One, and Uber. It sits at 27% acceptance, which means most candidates either time out or miss the core optimization. You'll see this when you're already deep in an online assessment and your brute force solution works on small inputs but fails on large ones. The trick isn't the logic, it's knowing which data structure makes the solution fast enough. If you blank on the pattern, StealthCoder runs invisibly during the assessment and surfaces a working approach in seconds.
Companies that ask "Count Prefix and Suffix Pairs II"
Count Prefix and Suffix Pairs II is the kind of problem that decides whether you pass. StealthCoder reads the problem on screen and surfaces a working solution in under 2 seconds. Invisible to screen share. The proctor sees nothing. Built by an Amazon engineer who realized the OA tests how well you memorized 200 problems, not how well you code.
Get StealthCoderThe naive approach counts every prefix-suffix pair by checking each one individually, but that's O(n squared) time with string operations on top. The actual solution uses a Trie or rolling hash to store prefixes and suffixes efficiently, letting you query in near-constant time whether a string can be built from a prefix-suffix combo. Rolling Hash is the elegant play here because you precompute forward and reverse hashes, then iterate once through candidates. Common failure: candidates build the right idea but don't optimize the hash comparisons, so they still TLE. This is the exact problem where an invisible StealthCoder hedge saves you from a failed OA, because the pattern isn't intuitive until you've seen it.
Pattern tags
You know the problem.
Make sure you actually pass it.
Count Prefix and Suffix Pairs II recycles across companies for a reason. It's hard-tier, and most candidates blank under the timer. StealthCoder is the hedge: an AI overlay invisible during screen share. It reads the problem and surfaces a working solution in under 2 seconds. Built by an Amazon engineer who realized the OA tests how well you memorized 200 problems, not how well you code. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Count Prefix and Suffix Pairs II interview FAQ
Why does the brute force approach fail on this problem?+
Checking every prefix-suffix pair naively is O(n squared) string comparisons, which times out on large inputs. You need a data structure like Trie or rolling Hash to make pair lookups constant or near-constant time. The acceptance rate of 27% reflects exactly this wall.
Is rolling hash or Trie the better approach here?+
Rolling Hash is faster in practice for this problem because you precompute hashes once and avoid tree traversal. Trie works but is slower on large character sets. Both are correct, hash collision risk is negligible with proper modulo.
Do I need to know this exact problem to pass at Uber or Autodesk?+
Not this exact problem, but yes, you'll see hard string matching questions with the same pattern at those companies. Knowing Trie and Rolling Hash cold is table stakes for their OAs.
What's the real trick to solving this in one pass?+
Store all prefixes and suffixes in a hash map first with their counts. Then iterate once, checking if each string can be split into a prefix and suffix that both exist. That's the O(n) insight most candidates miss.
How does this relate to other string matching problems I've seen?+
It combines prefix-suffix logic with hashing, similar to KMP failure function but using explicit hash storage instead. If you've solved problems on Trie or rolling hash separately, this merges both patterns under pressure.
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