Substring with Concatenation of All Words
A hard-tier problem at 33% community acceptance, tagged with Hash Table, String, Sliding Window. Reported in interviews at Roku and 9 others.
Substring with Concatenation of All Words is a hard-tier problem that's asked repeatedly at Amazon, Microsoft, Apple, and Meta. You're given a string and a list of words of equal length, and you need to find every index where a concatenation of all those words appears as a substring. The 33% acceptance rate reflects a real gap: most candidates know sliding window exists, but the word-boundary logic trips them up in interviews. If this problem lands in your OA and you can't nail the pattern, StealthCoder surfaces a working solution invisible to the proctor.
Companies that ask "Substring with Concatenation of All Words"
Substring with Concatenation of All Words 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 used it to pass JPMorgan's OA and system design loop.
Get StealthCoderThe trick is recognizing this as a sliding window problem where you slide by word length, not by character. You build a frequency map of the words you need to match, then slide a window across the string checking if each word-sized chunk matches your target. The naive approach scans every position and reconstructs every possible concatenation, which is slow. You instead precompute word frequencies and maintain a rolling window of words, comparing maps as you slide. Common failures: sliding by one character instead of word length, forgetting to handle overlapping word matches, or rebuilding the frequency map on every iteration instead of updating it incrementally. The hash table and sliding window work together to avoid redundant work. This is the kind of problem where the insight feels obvious once you see it, but blank-slate during the assessment, StealthCoder is your hedge.
Pattern tags
You know the problem.
Make sure you actually pass it.
Substring with Concatenation of All Words 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 used it to pass JPMorgan's OA and system design loop. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Substring with Concatenation of All Words interview FAQ
Why is this acceptance rate so low if it's just sliding window?+
The hard difficulty comes from the word-boundary constraint. You can't just slide character-by-character like a typical sliding window. You must slide by word length and match word frequencies, not substrings. Most candidates get the window idea but mess up the word-alignment logic or the frequency comparison step.
Do I really need a hash table if I'm already sliding a window?+
Yes. The hash table stores word frequencies so you can compare the window's contents against your target in constant time per slide. Without it, you'd rebuild or sort the window every iteration, which tanks your complexity. The hash table is what makes sliding window efficient here.
Is this still asked at FAANG companies?+
Frequently. Amazon, Microsoft, Apple, and Meta all report this problem. It tests whether you can combine multiple techniques (hash table, sliding window, string indexing) in a single solution. It's a filtering problem they use to separate solid mid-level candidates from weaker ones.
What's the main edge case I should watch?+
Words of length 1 and overlapping matches. If all words are single characters, your sliding window and word list handling must still align correctly. Also, multiple valid starting indices in the same string can trip up candidates who only return the first match or forget to collect all of them.
How does this relate to the other string problems?+
It combines hash table lookup speed with sliding window iteration discipline. Unlike simpler substring problems, you're not searching for a fixed pattern. You're assembling all permutations of a word list and finding where they appear. That assembly step is what most candidates underestimate.
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