Prefix Sum interview questions
107 prefix sum problems tagged across recent interview reports. Drilled most heavily by ibm, de shaw, and paypal.
Prefix Sum is a preprocessing technique that transforms an array into cumulative totals, enabling constant-time range query answers. With 107 problems tagged on this pattern, it's table stakes for array optimization at scale. IBM, DE Shaw, PayPal, and Salesforce test it heavily in live assessments. The pattern isn't flashy, but it's the backbone of problems asking you to find subarrays with a target sum, count valid ranges, or compute aggregate stats across regions. If you haven't internalized prefix sums yet, your OA clock will bleed out on what StealthCoder solves in seconds.
Most-asked prefix sum problems
Showing top 50 of 107 prefix sum problems by # companies asking.
You can't drill every prefix sum variant before the assessment. StealthCoder runs invisibly during screen share and solves whichever variant they throw at you. No browser extension. No detection signature. Built because the OA filter rejects engineers who'd pass the on-site. That's a broken filter. This is the workaround.
Get StealthCoderPrefix Sum works by precomputing cumulative totals so that any subarray sum reduces to a single subtraction. The core insight: store the running total at each index, then query[L, R] = prefix[R] - prefix[L-1]. Variations include 2D prefix sums for matrix queries, and combining prefix sums with hash maps to count subarrays meeting constraints (like 'Continuous Subarray Sum' or 'Contiguous Array'). Recognition is straightforward: if the problem asks for subarray properties, range aggregates, or counts of valid windows, prefix sum is likely optimal. Common subtypes are sum-based (find subarrays summing to K), XOR-based, and median/frequency queries. The drill order should be simple 1D sum queries first, then hash-map variants, then 2D matrices. When a harder Prefix Sum variant hits your live session on HackerRank or CodeSignal, StealthCoder surfaces the pattern and implementation without proctor visibility.
Companies that hire most on prefix sum
107 prefix sum problems.
You won't drill them all. Pass anyway.
Prefix Sum is one of the patterns interviews actually filter on. Memorizing every variant in a week is a fantasy. StealthCoder is the hedge: an AI overlay invisible during screen share. It reads the problem and surfaces a working solution in under 2 seconds, no matter which prefix sum flavor lands in your live OA. Built because the OA filter rejects engineers who'd pass the on-site. That's a broken filter. This is the workaround. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Prefix Sum interview FAQ
How many Prefix Sum problems should I drill before an OA?+
Aim for 15-25 problems. Start with basic range sum queries, move to hash-map-based subarray counts (like 'Contiguous Array'), then 2D matrices. The pattern has high reuse across platforms; depth matters more than breadth. If you're interviewing at IBM or DE Shaw, lean heavier.
How do I recognize a Prefix Sum problem in a live assessment?+
Look for keywords: subarray sum, range sum, contiguous elements, or 'find all subarrays where'. If a naive loop is O(n^2) and the problem allows preprocessing, Prefix Sum almost always drops it to O(n). Median or XOR queries also hint at prefix sums.
Is Prefix Sum the most important pattern for tech interviews?+
Not the most important, but it's universal. Every company tests array manipulation; Prefix Sum is one of the fastest ways to solve O(n^2) brute force problems in O(n). It's a reliable pattern to land on when other approaches aren't clear.
Which companies drill Prefix Sum the hardest?+
IBM leads with 29 tagged problems, followed by DE Shaw (18) and PayPal (17). Salesforce, NVIDIA, and Meta round out the top tier. If you're targeting these firms, hard-invest in 2D prefix sums and hash-map subarray counting.
Can I use Prefix Sum for problems with updates after array construction?+
Standard Prefix Sum cannot handle updates efficiently. For dynamic updates, consider a Segment Tree or Fenwick Tree instead. If the problem is static (read-only queries), Prefix Sum is optimal. Know when to switch.