A prefix sum array stores sums up to each index. After O(n) preprocessing, you can answer range sum queries in O(1): sum[l..r] = prefix[r] - prefix[l-1]. It’s also used for counting frequencies and quick “how many in a range” queries.
Advanced answer
Deep dive
Expanding on the short answer — what usually matters in practice:
Complexity: compare typical operations (average vs worst-case).
Invariants: what must always hold for correctness.
When the choice is wrong: production symptoms (latency, GC, cache misses).
Explain the "why", not just the "what" (intuition + consequences).
Trade-offs: what you gain/lose (time, memory, complexity, risk).
Edge cases: empty inputs, large inputs, invalid inputs, concurrency.
Examples
A tiny example (an explanation template):
// Example: discuss trade-offs for "what-is-a-prefix-sum-array-and-what-does-it-spee"
function explain() {
// Start from the core idea:
// A prefix sum array stores sums up to each index. After O(n) preprocessing, you can answer
}
Common pitfalls
Too generic: no concrete trade-offs or examples.
Mixing average-case and worst-case (e.g., complexity).