Amazon

ApproachOne pass with a hash map of value → index; for each x, check whether target − x was already seen.

EvaluatesWhether you move from the brute-force O(n²) to a clean O(n) hash-map solution and reason about edge cases.

ApproachFlood-fill each unvisited land cell with BFS/DFS and mark visited; count how many fills you start.

EvaluatesGraph traversal on a grid, visited-state management, and complexity analysis.

ApproachCombine a hash map with a doubly linked list; move touched nodes to the front, evict from the tail.

EvaluatesComposing data structures to hit tight time bounds and getting the eviction bookkeeping right.

ApproachTalk through the API, a hash/base-62 key scheme, storage and read/write ratio, caching hot links, and redirects.

EvaluatesScoping, key generation, read-heavy scaling, and caching decisions.

ApproachCompare token bucket vs. sliding window, where state lives (per-node vs. Redis), and behavior under bursts.

EvaluatesAlgorithm choice, distributed state, and failure/consistency trade-offs.

ApproachMap it to the Leadership Principles (Dive Deep, Ownership): show data, the investigation, and the fix.

EvaluatesWhether you operate the way Amazon writes its Leadership Principles — data-driven and owner-minded.

ApproachSTAR with a bias-for-action angle; quantify the outcome.

EvaluatesBias for Action and Deliver Results under ambiguity.

ApproachUse STAR: the situation, your reasoning, how you disagreed respectfully and reached a decision, and the result.

EvaluatesCommunication, judgment, and whether you can disagree-and-commit without drama.