30 Apple LeetCode Interview Questions for 2026

Practice 30 Apple LeetCode interview questions, from arrays and trees to LRU Cache and dynamic programming, plus a 2026 prep plan and loop breakdown.

Apple LeetCode Interview Questions: 30 Most Asked (2026)

If you're preparing for an Apple coding interview, you want to know which LeetCode problems actually show up — not a generic FAANG list, not a motivational pep talk. This guide covers 30 frequency-informed Apple leetcode interview questions, a clear picture of how Apple's loop works for both new grads and experienced engineers, and a practical study plan. Everything here draws from community-reported frequency data, candidate experience threads, and 2026 prep signals.

How Apple's interview loop actually works

Apple's process is more fluid than most FAANG loops. The exact structure depends on the team, the hiring manager, and the role level.

Screening phase

The loop starts with a recruiter call, followed by a hiring manager screen — typically 30 to 45 minutes. Some teams include an early coding or language-specific check (Java is common) at this stage. The hiring manager screen is partly technical, partly motivational: they want to know why Apple, why this team, and whether your experience maps to the role.

Technical rounds

Expect 3 to 5 coding rounds, each about an hour. Some teams schedule rounds across multiple days, including evening slots. The mix varies, but common patterns include algorithm implementation, BFS and graph traversal, dynamic programming, OOP design, and system design scoped to the team's actual domain — not always a textbook "design Twitter" prompt. One detailed candidate account of an ICT4 (senior) role in London described 8 to 9 rounds spread across several weeks, covering topological sorting, BFS with follow-ups, OOP class design, and a system design round tied directly to the team's product area.

People fit and senior director rounds

Apple cares about behavioral and motivation signals alongside technical depth. For experienced hires (ICT4 and above), expect a senior director or manager chat focused on career story, motivation, and people fit. This is not a formality — it's a real evaluation gate.

Apple LeetCode interview questions — the 30 most asked

The list below is built from community-tagged Apple problems, frequency data aggregated across candidate reports, and 2026 signals from r/leetcode. The difficulty distribution skews toward Medium: roughly 11 Easy, 25 Medium, and 8 Hard across the ~44 most commonly cited problems. Design-style questions appear with notably high frequency in recent reports.

This is community-reported data, not an Apple-official list. Treat it as directional — the best available signal for where to focus your practice.

Arrays and strings (core, high frequency)

• Two Sum (Easy) — The warm-up that still appears. Tests hash map fundamentals and edge-case awareness.
• Best Time to Buy and Sell Stock (Easy) — Single-pass greedy logic. Apple asks this and its variants frequently.
• 3Sum (Medium) — Two-pointer technique after sorting. Tests your ability to handle duplicates cleanly.
• Product of Array Except Self (Medium) — Prefix/suffix array thinking without division.
• H-Index (Medium) — Sorting or counting-based approach. Shows up more at Apple than at other companies.
• Longest Substring Without Repeating Characters (Medium) — Sliding window. Core pattern for string problems.
• Group Anagrams (Medium) — Hash map with sorted-key trick. Clean implementation matters.
• Valid Parentheses (Easy) — Stack fundamentals. Often used as a warm-up before harder follow-ups.

Trees and graphs

• Sum Root to Leaf Numbers (Medium) — Community-reported at 91% frequency for Apple. DFS with path accumulation.
• Check Completeness of a Binary Tree (Medium) — BFS level-order traversal with a null-check pattern. Reported at 87% frequency.
• Course Schedule (Medium) — Topological sort via BFS or DFS. Apple rounds have included this with follow-up questions about cycle detection.
• Word Ladder (Hard) — BFS on an implicit graph. Tests your ability to model the problem before coding.
• Binary Tree Level Order Traversal (Medium) — Straightforward BFS, but Apple interviewers often layer follow-ups on top.
• Lowest Common Ancestor of a Binary Tree (Medium) — Recursive DFS. Clean base-case handling is what separates good answers.
• Number of Islands (Medium) — Grid BFS/DFS. A staple across FAANG, and Apple is no exception.

Dynamic programming and binary search

• Maximum Profit in Job Scheduling (Hard) — Reported at 100% frequency in community-aggregated Apple data. DP with binary search on sorted intervals.
• Coin Change (Medium) — Classic unbounded knapsack. Tests whether you can articulate the recurrence clearly.
• Longest Increasing Subsequence (Medium) — DP or patience sorting. Apple has asked this with follow-ups about O(n log n) optimization.
• House Robber (Medium) — Simple 1D DP, but interviewers probe whether you can extend it to circular variants.
• Search in Rotated Sorted Array (Medium) — Modified binary search. Clean invariant reasoning matters more than the code.
• Find Minimum in Rotated Sorted Array (Medium) — Pairs naturally with the above. Binary search edge cases are the real test.

Design problems (especially high Apple frequency)

2026 community reports flag design questions as disproportionately common at Apple. If you're short on time, prioritize this section.

• LRU Cache (Medium) — Hash map + doubly linked list. Apple's most consistently reported design problem.
• Time Based Key-Value Store (Medium) — Binary search on timestamps within a hash map structure. High frequency in recent Apple reports.
• Design Add and Search Words Data Structure (Medium) — Trie with wildcard DFS. Tests both data structure choice and recursive traversal.
• Design Hit Counter (Medium) — Queue or circular buffer. Clean time-window logic.
• Serialize and Deserialize Binary Tree (Hard) — BFS or DFS with delimiter handling. Tests end-to-end implementation discipline.

Hash table and miscellaneous

• Top K Frequent Elements (Medium) — Heap or bucket sort. Apple interviewers often ask about the time-complexity trade-off between approaches.
• Contains Duplicate (Easy) — Set-based check. Simple, but sometimes used as a lead-in to harder follow-ups.
• Merge Intervals (Medium) — Sort + linear scan. Appears across multiple FAANG companies; Apple included.
• Trapping Rain Water (Hard) — Two-pointer or stack approach. Tests your ability to reason about invariants under pressure.

Fresher vs. experienced — what changes

New grad / intern track

The focus is on clean algorithmic fundamentals: arrays, strings, trees, hash tables. Expect 2 to 3 coding rounds. Communication and approach matter as much as the final solution — Apple interviewers want to hear you think out loud, not just watch you type. LeetCode Easy and Medium is the core target. A few Hards may appear, but they're the exception.

Experienced hire (5+ years, ICT4 and above)

The loop gets longer — sometimes significantly. One candidate with 6+ years of SDE experience and 1,192 LeetCode problems solved went through 8 to 9 rounds across multiple weeks before receiving an ICT4 offer. System design and OOP design rounds are added to the loop. Behavioral depth is expected: career story, motivation, and people-fit signals carry real weight. And rejection is possible even with strong experience — a community-reported account of a Developer Technology Engineer candidate with 10 years of experience ended in rejection. The bar is high and team-specific.

Apple interview questions beyond LeetCode

Apple's process isn't purely algorithmic. Here's what else gets tested:

• Iterative vs. recursive trade-offs. Interviewers ask you to implement both, then discuss when each is appropriate. This appeared in the ICT4 London loop.
• OOP design. Class structure, extensibility, design patterns. Not a system design round — a code-level design round.
• System design scoped to the team's domain. Apple teams often ask about systems they actually build, not generic textbook prompts. Prepare by researching the team's product area.
• Behavioral (STAR format). Career trajectory, conflict resolution, motivation. For senior roles, this is a full round, not a five-minute warm-up.
• Testing mindset (SDET roles). Apple SDET interviews evaluate how you think as an engineer who tests — engineering judgment and testing strategy, not just coding speed.

How to prepare — a practical study plan

Build the algorithmic foundation first

Use the 30 problems above as your practice queue. Prioritize the topic areas Apple hits hardest: arrays, strings, trees, graphs, DP, and design. Solve timed — Apple rounds are one hour, and pacing matters. If you can't finish a Medium in 25 minutes with clean code and a verbal walkthrough, that's the gap to close.

Add system design and behavioral early, not at the end

LeetCode alone isn't enough. Candidates who convert Apple interviews into offers pair coding practice with system design prep and STAR-format behavioral stories from the start. Practice explaining trade-offs out loud — not just writing code silently. The ability to articulate your reasoning while coding is what Apple interviewers are actually scoring.

Run mock interviews before the real loop

Practicing alone beats not practicing. Practicing with feedback beats practicing alone. Verve AI's mock interview tool lets you run Apple-style coding and behavioral rounds with real-time feedback and structured performance reports — no need to coordinate schedules with a human partner. And if you want real-time support during the actual interview, the Interview Copilot listens to the conversation and suggests answers as you go, invisible to the interviewer via Stealth Mode on the Desktop app.

Wrapping up

Apple's loop is team-specific, design-heavy, and longer than most candidates expect. The 30 problems above cover the highest-frequency patterns reported by the community. Pair them with system design prep, behavioral stories, and timed practice under realistic conditions. If you want to sharpen your answers with AI-driven mock interviews or have a copilot ready for the real thing, Verve AI is built for exactly that.