L62 · Company-Tagged — Microsoft & Amazon
Work the frequency-ranked lists for the two companies that matter most to you, and adapt to each one's distinct interview style rather than treating all loops as identical.
🎯 Stop grinding random problems and start working the two companies' frequency-ranked lists, adapting your delivery to each one's distinct interview culture.
Series: LeetCode — From Basics to Interview-Ready · Session 62 / 65 · Phase 4 · Interview simulation
Prerequisites
You should have finished Phases 1–3 (patterns through advanced). This is not a new-pattern session — it's a targeting session. You already own the techniques; now you point them at the specific problem distributions and interview styles of the two companies most people in this series are aiming at.
Watch first (skim before the session)
Why this session exists
By this point you've drilled patterns in isolation. Interviews don't hand you a labelled pattern — they hand you a story, and they're tagged by company because different companies pull from different corners of the problem space and grade differently. This session is about targeting: work the frequency-ranked list for your target company, and rehearse the delivery style that company rewards.
The two companies here have genuinely different cultures, and treating an Amazon loop like a Microsoft loop (or vice versa) leaves marks on the table.
Microsoft: style and staples
Microsoft interviews tend to be conversational and extension-driven. A single problem often grows: you solve the base case, then they add a constraint ("now the input streams in", "now do it in place", "now handle duplicates"). They care about clean code, correct edge cases, and how gracefully you adapt when the problem mutates mid-interview.
Staple problems (real LeetCode, classically Microsoft-tagged):
- LC151 · Reverse Words in a String — in-place-ish string surgery; watch multiple spaces.
- LC54 · Spiral Matrix — careful boundary bookkeeping; the extension is "generate" (LC59).
- LC48 · Rotate Image — in-place 90° rotation; the transpose-then-reverse trick.
- LC236 · Lowest Common Ancestor of a Binary Tree — the recursive return-up pattern.
- LC138 · Copy List with Random Pointer — hashmap or the interleave trick.
- LC146 · LRU Cache — hashmap + doubly-linked list; a design classic they love.
- LC33 · Search in Rotated Sorted Array — binary search with the sorted-half decision.
- LC53 · Maximum Subarray — Kadane; extension to "return the subarray".
Amazon: style and staples
Amazon interviews wrap DSA in Leadership-Principles framing and value clear narration, explicit trade-offs, and edge-case discipline. The coding bar is medium-heavy; the differentiator is often how you communicate — stating assumptions, calling out complexity, and connecting your choices to customer impact.
Staple problems (real LeetCode, classically Amazon-tagged):
- LC200 · Number of Islands — the single most-asked Amazon graph problem; DFS/BFS flood fill.
- LC56 · Merge Intervals — sort-then-sweep; the canonical interval problem.
- LC146 · LRU Cache — design; also huge at Amazon.
- LC127 · Word Ladder — BFS shortest transform (Hard).
- LC973 · K Closest Points to Origin — heap / quickselect.
- LC1 · Two Sum — still asked as a warm-up; nail it in 60 seconds.
- LC994 · Rotting Oranges — multi-source BFS.
- LC207 · Course Schedule — topological sort / cycle detection.
From first principles: why company-tagged practice works
- 1Interview problem distributions are NOT uniform — each company pulls disproportionately from certain patterns.forced by · Teams reuse question banks and cultural staples, so a handful of patterns account for most of what you'll actually see.
- 2Your remaining prep time is scarce and has diminishing returns on already-strong patterns.forced by · The marginal value of your Nth flanker-problem is tiny; the value of your first Number-of-Islands before an Amazon loop is huge.
- 3So you should weight practice by (company frequency) × (your current weakness), not by novelty.forced by · That maximises expected interview coverage per hour spent — an explicit expected-value calculation.
- 4Delivery style is separately gradable and company-specific, so rehearse it explicitly.forced by · Two candidates with identical code get different scores based on narration, trade-off articulation, and follow-up handling.
Mental model
- Pick the target company; pull its frequency-ranked, company-tagged list.
- Filter to your WEAK patterns first — strong patterns have low marginal value now.
- For Amazon: narrate brute-force → complexity → insight → optimised complexity, out loud.
- For Microsoft: solve, then proactively invite the extension.
Memory hook
"Amazon: narrate and dive deep. Microsoft: solve then stretch." Two four-word cues, one per company, capturing the delivery difference. On the morning of a loop, recite the one for that company; it primes the behaviour the graders actually reward.
What interviewers actually ask (mixed mock)
Run these as a timed mock, alternating the two styles. For each, state complexity out loud and name at least one edge case before coding.
- LC200 · Number of Islands — Amazon. DFS/BFS flood fill. Probe: grid traversal + visited handling; follow-up: count island shapes (LC694) or largest island (LC695).
- LC56 · Merge Intervals — Amazon, Microsoft. Sort by start, sweep. Probe: the overlap condition; follow-up: insert one interval into a sorted set (LC57).
- LC54 · Spiral Matrix — Microsoft. Boundary shrinking. Probe: off-by-one on the four edges; follow-up: generate the spiral (LC59).
- LC146 · LRU Cache — both. Hashmap + DLL, all ops O(1). Probe: the eviction wiring; follow-up: LFU (LC460, Hard).
- LC33 · Search in Rotated Sorted Array — Microsoft. Which half is sorted. Probe: the pivot logic; follow-up: with duplicates (LC81).
- LC973 · K Closest Points to Origin — Amazon. Max-heap of size k or quickselect. Probe: why a heap beats a full sort; follow-up: quickselect for average O(n).
The escalation is always there — have the follow-up ready before they ask. That readiness is what "senior signal" looks like.
Tradeoff
In practice
Frequency-ranked company lists are a real and legitimate prep tool (LeetCode's own company tags, and community-maintained frequency lists). The signal is noisy — banks rotate, and no list is a guarantee — so treat it as a weighting, not a script. The durable habit this session builds is meta: near any high-stakes evaluation, spend your last scarce hours on the intersection of "most likely to appear" and "where I'm weakest", and rehearse the delivery the evaluator rewards. That transfers well beyond coding interviews.
- Name 5+ frequency staples for each of Amazon and Microsoft without looking.
- Articulate the delivery difference: Amazon narrate/dive-deep vs Microsoft solve-then-stretch.
- For any staple, state brute-force complexity, the insight, optimised complexity, and one follow-up — out loud.
- Explain why last-mile prep weights by frequency × your weakness, not novelty.
- Run a 45-minute two-problem mock alternating the two companies' styles.
Practice queue
Run as a timed mock in this order; log each as cold / warm / hint / failed, and note whether you narrated + handled the follow-up.
- LC200 Number of Islands — Amazon staple.
- LC56 Merge Intervals — both.
- LC54 Spiral Matrix — Microsoft staple.
- LC146 LRU Cache — design, both.
- LC33 Search in Rotated Sorted Array — Microsoft.
- LC973 K Closest Points to Origin — Amazon.