Amazon Coding Interview Prep: What They Actually Test in 2026

· Written by

Amazon runs the highest-volume software engineering interview pipeline on the planet, which makes it unusually predictable: the company has documented mechanisms for everything, including how it interviews. That’s good news for you. The Amazon coding interview isn’t harder than Google’s or Meta’s, but it is different, because roughly half of your evaluation has nothing to do with code.

Here’s what the loop actually looks like in 2026, what they test, and how to prepare for both halves.

The Amazon Interview Format

A typical SDE loop runs in three stages:

  1. Online Assessment (OA). Two coding problems (usually easy-to-medium) in about 90-105 minutes, plus a work-style survey and sometimes a system-behavior simulation. This is a filter, not a differentiator: you need clean, working solutions, not clever ones.
  2. Phone screen. One 45-60 minute interview: a coding problem in a shared editor plus 15-20 minutes of Leadership Principle (LP) questions.
  3. Onsite loop (“the loop”). Four to five hour-long interviews: typically two coding rounds, one system design round (SDE II and above; new grads often get a lighter “logical and maintainable code” round instead), and one round led by a Bar Raiser, a trained interviewer from outside the hiring team with veto power. Every round includes LP questions, usually the first 20-25 minutes.

Do the math on that onsite: across five interviews, you’ll spend roughly two hours answering behavioral questions and two-and-a-half solving technical problems. Candidates who prepare only for the coding half are preparing for half the interview.

The DSA Topics That Actually Show Up

Amazon’s question distribution is stable year over year, and it skews practical. You’ll see far more “process this stream of orders” than “prove this clever invariant.” Based on recurring interview reports, the topics worth the most preparation time, in rough order:

  1. Hash maps and sets: the single most common building block. Frequency counting, deduplication, grouping, caching. If you’re shaky here, start with Day 23: Hash Tables and our deep dive on hash map interview problems.
  2. Arrays and strings with two pointers / sliding window: substring problems, interval merging, in-place manipulation. Foundations in Day 4: Introduction to Arrays.
  3. Trees and BSTs: traversals, lowest common ancestor, validation. See Day 14: Binary Trees and Day 16: Tree Traversals.
  4. Graphs and BFS/DFS: Amazon loves grid problems (“Number of Islands” and its many warehouse-flavored reskins: rotting oranges, zombie servers, package routing). Covered in Day 18: Graphs Introduction and Day 20: Graph Traversals.
  5. Heaps and top-K problems: “K closest warehouses,” “K most frequent products.” Almost a rite of passage. See Day 17: Heaps.
  6. Linked lists, stacks, queues: LRU cache is a perennial favorite that combines a hash map with a doubly linked list (Day 9 covers the list half).
  7. Dynamic programming: appears less often than at Google or Meta, and usually in classic forms. Day 30 onward covers the canon.

A Worked Example: Top-K Frequent Products

This pattern (heap plus hash map) covers a whole family of Amazon questions:

 1import heapq
 2from collections import Counter
 3
 4def top_k_frequent(products: list[str], k: int) -> list[str]:
 5    """Return the k most-ordered products; ties broken alphabetically."""
 6    counts = Counter(products)  # O(n) frequency map
 7
 8    # Min-heap of size k: (count, reversed-alphabetical) so the
 9    # *least* qualifying product sits at the top, ready to evict.
10    heap = []
11    for product, count in counts.items():
12        heapq.heappush(heap, (count, [-ord(c) for c in product], product))
13        if len(heap) > k:
14            heapq.heappop(heap)
15
16    # Pop k winners, then reverse: most frequent first.
17    result = [heapq.heappop(heap)[2] for _ in range(len(heap))]
18    return result[::-1]

That’s O(n log k) time and O(n) space, and saying those two facts out loud, unprompted, is exactly the kind of signal Amazon interviewers write down. Note the tie-breaking detail: Amazon problem statements are wordier than LeetCode’s and often hide a requirement like “ties broken alphabetically” mid-paragraph. Read the prompt twice.

What the Coding Rounds Grade

Interviewers submit written feedback against explicit criteria: problem-solving approach, coding fluency, communication, and testing. Two habits pay outsized dividends:

  • Narrate the plan before code. State the brute force, its complexity, then the optimization. Amazon interviewers are trained to probe “why,” not just “what.”
  • Test your own code. Walk through an example, then name edge cases: empty input, single element, ties, overflow. Candidates who find their own bug score better than candidates with no bugs and no testing.

Leadership Principles: The Half Everyone Underprepares

Amazon has 16 Leadership Principles, and LP questions are behavioral: “Tell me about a time you disagreed with your manager,” “Tell me about a time you missed a deadline.” Each interviewer is assigned 2-3 LPs to probe, and their written feedback quotes your answers. Vague answers aren’t neutral. They’re negative data.

You don’t need a story per principle. You need 6-8 strong stories, each mapped to 2-3 principles, prepared in STAR format:

  • Situation: one sentence of context.
  • Task: what you were responsible for.
  • Action: what you did (this should be 60% of the answer, and “I,” not “we”).
  • Result: quantified. Amazon culture runs on numbers: latency shaved, revenue saved, tickets reduced.

The LPs that come up most for engineers: Customer Obsession, Ownership, Dive Deep, Deliver Results, Disagree and Commit, Bias for Action, and Invent and Simplify. Build your story bank so every one of those has coverage, and expect follow-ups three questions deep (“What would you do differently?” “What did your manager say?”). Follow-ups are where invented stories collapse. Use real ones, including at least one genuine failure. “Tell me about a time you failed” is close to guaranteed, and the interviewer is grading what you learned, not the failure itself.

One more mechanism worth knowing: the Bar Raiser exists to keep hiring standards independent of team pressure. They’ll typically dig hardest on LPs and on your reasoning under follow-up. There’s no trick to beating them, just depth. Real stories, real numbers, real lessons.

Common Ways Strong Candidates Fail

  • All LeetCode, no LPs. The most common failure mode by a wide margin. A “Hire” on coding plus two “No Hire” LP scores is a rejection.
  • Jumping into code silently. Amazon rounds are conversational; ten silent minutes of typing reads as a communication risk.
  • Ignoring the wordy prompt. Amazon questions come wrapped in scenarios. Requirements hide in the prose.
  • “We” answers. If the interviewer can’t tell what you did, the story counts for nothing.
  • No questions asked. Not clarifying inputs, scale, or edge cases before coding is a Dive Deep miss before you’ve written a line.

A 60-Day Amazon Prep Plan

The coding syllabus above maps cleanly onto a day-per-topic structure, which is exactly how our 60-day curriculum is sequenced:

  • Days 1-12 (foundations): arrays, time complexity, linked lists, stacks, queues. This is where LRU-cache fluency comes from.
  • Days 13-24 (the Amazon core): trees, heaps, graphs, hash tables, sets. If your time is short, spend it here.
  • Days 25-47 (patterns): binary search, sorting, dynamic programming, greedy, shortest paths.
  • Days 48-60 (advanced): backtracking, bit manipulation, tries.

Layer the behavioral prep alongside: write two STAR stories per week, and from week four onward, rehearse them out loud, ideally in mock interviews, since the pause-and-probe rhythm of LP follow-ups is a skill of its own. For how this fits a realistic calendar (most candidates need 3-6 months end to end), see how long it takes to get a FAANG offer, and for the full resource landscape, our tiered guide to FAANG prep resources.

The short version: Amazon is the most learnable FAANG interview. The coding bar is medium-difficulty problems executed cleanly and explained well; the behavioral bar is a prepared story bank with real numbers. Start Day 1 free and give the LPs the same respect you give the algorithms. That combination is rarer than it should be.

Good luck with the loop, and remember: at Amazon, the story about the outage you owned is worth as much as the algorithm that prevents the next one.