Apple Coding Interview Questions: Patterns and Top Problems

Apple’s tagged problem list looks different from the other big companies. The top of the list is full of build-something problems. LRU Cache, Merge Intervals, and Design Hit Counter all outrank the usual warmups, and Easy questions barely appear. The loop rewards engineers who can turn a small system description into working code.

This page ranks the coding questions that show up most often in Apple’s tagged problem data. Each row links to the pattern guide on this site that teaches the technique. Later sections break down the topic weights and turn them into a study order.

The short version

• Medium problems hold 77 percent of the tagged set. Easy holds only about 10 percent.
• Arrays touch about 51 percent of tagged problems. Hash tables reach about 36 percent.
• Seventeen of the top 20 sit in the very top demand band. Design-flavored builds lead the list.

Apple’s Interview Loop

• A recruiter call confirms your background and the team you are interviewing for.
• One or two technical phone screens follow, each with live coding.
• The onsite loop runs several rounds in one day for most roles.
• Several rounds are pure coding. Questions often lean toward data structure design rather than puzzle tricks.
• At least one round usually digs into your past projects in depth.
• Teams tailor later rounds to their stack, which is less common at other large companies.

That team-specific tailoring changes how you should read the list below. It reflects what Apple asks across all teams. If your interviews target one product group, weight the patterns that group cares about even heavier.

The Questions Apple Asks Most

LeetCode tags problems by company when candidates report them. Every tagged problem carries a relative frequency score from 0 to 100. These 20 score highest across a recent tagging window for Apple. Seventeen of them sit in the very top band. Each row links to our pattern guide for that technique.

If you learn five guides first, learn these

1. Open with linked list . The list leader needs it, and so does Reverse Linked List.
2. Pair it with hash table . Hash tables reach about 36 percent of tagged problems.
3. Take graph traversal next. Grids and course logic touch about 30 percent combined.
4. Add interval scheduling for Merge Intervals and the calendar variants that product teams favor.
5. End with binary search for Time Based Key-Value Store and Find First and Last Position of Element in Sorted Array.

Count the design-flavored entries in the top five. LRU Cache alone tests hash maps, linked lists, and constant time removal at once. Time Based Key-Value Store adds binary search over timestamps. Apple uses these problems because they mirror daily work closer than an abstract puzzle does. Prepare to state tradeoffs out loud while you code, not only after.

Topics and Difficulty at Apple

The most common topics among tagged Apple problems appear below. The share is the percent of tagged problems that carry the topic. One problem can carry several topics, so the shares add to more than 100 percent.

Sorting carries no link because we treat it as part of other patterns rather than a pattern of its own. Two rows deserve a second look. Hash tables reach 36 percent here, well above Google’s 21 percent, because the design classics depend on them. Graph traversal combines breadth-first and depth-first search into roughly three of every ten tagged problems, mostly through grid problems like Number of Islands and Course Schedule II.

Difficulty splits harder than the other guides on this site:

Only about one tagged problem in ten is Easy, against a third at Google and more than a quarter at Amazon. Apple skips the warmups and starts near working difficulty. Your prep should assume the first question already requires a real template executed cleanly.

Method: LeetCode company-tagged lists for Apple, ranked by relative frequency scores from 0 to 100. Shares overlap because one problem can carry several topics.

Where to Spend Your Prep Time

1. Learn the linked list and hash map pairing first. LRU Cache sits at the very top of the list, and Reverse Linked List backs it up. The linked list and hash table guides cover both halves.
2. Study graph traversal until grids feel routine. BFS and DFS together touch nearly a third of tagged problems. The graph traversal and topological sort guides handle Course Schedule II and Number of Islands.
3. Own interval logic. Merge Intervals ranks second overall, and calendar style variants appear often at product teams. The interval scheduling guide teaches the sort-then-scan form.
4. Narrate tradeoffs while you code. Apple interviewers listen for engineering judgment during the build, not just a correct ending. Say why you picked each data structure as you pick it.
5. Keep templates fresh with scheduled reviews. Design problems decay fastest because they hold many small details. Spaced repetition returns each piece right before you would lose it.

    graph TD
    A["Linked list plus hash table"] --> B["Graph traversal for grids"]
    B --> C["Interval sort-then-scan"]
    C --> D["Narrate tradeoffs while coding"]
    D --> E["Top twenty written from memory"]
    E --> F["Scheduled review until the onsite"]
  

The diagram runs the prep plan from core builds to narrated timed practice.

Questions Candidates Ask

Why does Apple ask so few Easy questions?

Only about one tagged problem in ten ranks Easy. That is far below the share at Google or Amazon. Expect real templates from the first question.

What makes the design problems different?

They test several skills at once. LRU Cache alone needs hash maps, linked lists, and constant time removal. State tradeoffs out loud while you code, since interviewers listen for judgment during the build.

Which pattern should I learn first?

Start with the linked list and hash table pairing. The top row needs both halves. Then take graph traversal for Number of Islands and Course Schedule II.

Next Steps

Walk the top 20 table and open any guide whose template you cannot write from memory. The coding patterns index lists all of them, and linked list plus graph traversal are the two highest-yield reads for Apple specifically. Interviewing at several companies? See how the topic weights shift in the Google , Meta , and Amazon guides.