Number of Subarrays That Match a Pattern II
A hard-tier problem at 32% community acceptance, tagged with Array, Rolling Hash, String Matching. Reported in interviews at ThoughtWorks and 1 others.
Number of Subarrays That Match a Pattern II is a hard problem that combines rolling hash with pattern matching on arrays, not strings. You're given an array of integers and need to count subarrays matching a binary pattern (whether each element is greater than the next). ThoughtWorks and Autodesk have asked this. The acceptance rate sits at 32%, which means most candidates either don't recognize the rolling hash angle or apply it incorrectly to the array domain. If this hits your live assessment and you're stuck between brute force and optimization, StealthCoder surfaces a working hash-based solution in seconds, invisible to the proctor.
Companies that ask "Number of Subarrays That Match a Pattern II"
Number of Subarrays That Match a Pattern 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. Made by a working FAANG engineer who treats the OA the way companies treat hiring: as a game with rules you should know.
Get StealthCoderThe trap is treating this like a string-matching problem when it's really about hashing relationships between consecutive elements. You convert the array into a pattern of comparisons (greater-than, less-than), then use rolling hash to find all matching windows. Most candidates build the pattern correctly but fail on hash collision handling or window size management. The rolling hash lets you avoid O(n*m) substring comparisons. The key insight: hash the pattern incrementally as you slide the window, not the raw array values. Common pitfall is recalculating the hash from scratch for each window instead of rolling forward. When you're live on the OA and pattern matching logic feels stuck, StealthCoder handles the hash computation and window logic, letting you move on.
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Number of Subarrays That Match a Pattern 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. Made by a working FAANG engineer who treats the OA the way companies treat hiring: as a game with rules you should know. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Number of Subarrays That Match a Pattern II interview FAQ
Is this really asked at FAANG or just niche companies?+
ThoughtWorks and Autodesk have confirmed asks. It's not in the mainstream FAANG rotation, but it appears in assessments targeting systems and backend roles that value efficient hashing. At 32% acceptance, it's a strong differentiator if you nail it.
What's the actual trick here?+
The trick is rolling hash on the pattern itself, not raw array values. You build a binary pattern from comparisons, hash it as you slide a fixed-size window, and count matches. Most fail because they either skip rolling hash entirely or misapply it to the values instead of the pattern.
How hard is the implementation once you see the pattern?+
The logic is straightforward: build pattern, implement rolling hash with a good prime/base, slide and compare hashes. The hard part is visualizing it before you code. Once you know rolling hash is the path, implementation takes 15-20 minutes for most engineers.
How does this relate to String Matching and Hash Function topics?+
String Matching teaches you the pattern-matching framework and why rolling hash beats naive comparison. Hash Function is the core tool. You're applying string-matching intuition to an array problem by converting comparisons into a pattern string, then hashing it.
What happens if I try brute force instead?+
Brute force is O(n*m) where m is pattern length, which fails on larger inputs. Rolling hash drops it to O(n+m) amortized. At 32% acceptance, the test data likely includes cases where brute force times out, forcing you to optimize.
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