Prepare for your Amazon coding interview

Algorithms and data structure rounds calibrated to Amazon's focus on leetcode-style problems, system scale thinking, and production-ready code.

Socratify AI

The Interview

What Amazon is looking for

Arrays Interview

Array & Sorting Problems

01Two Pointer Technique

02Sliding Window

03Sorting & Searching

04Subarray Logic

Graphs Interview

Graph & Tree Problems

01BFS/DFS Traversal

02Graph Connectivity

03Tree Recursion

04Path Finding

Dynamic Prog Interview

Dynamic Programming Problems

01Subproblem Decomposition

02Memoization

03DP State Definition

04Optimization Patterns

Amazon heavily weights leetcode-medium and hard problems//Focus on efficient implementations and scale thinking//Leadership Principles embedded in behavioral questions

Practice Library

Amazon exercises

Ready to ace your Amazon coding interview?

Prepare for your Amazon coding interview

Algorithms and data structure rounds calibrated to Amazon's focus on leetcode-style problems, system scale thinking, and production-ready code.

Socratify AI

The Interview

What Amazon is looking for

Arrays Interview

Array & Sorting Problems

01Two Pointer Technique

02Sliding Window

03Sorting & Searching

04Subarray Logic

Graphs Interview

Graph & Tree Problems

01BFS/DFS Traversal

02Graph Connectivity

03Tree Recursion

04Path Finding

Dynamic Prog Interview

Dynamic Programming Problems

01Subproblem Decomposition

02Memoization

03DP State Definition

04Optimization Patterns

Amazon heavily weights leetcode-medium and hard problems//Focus on efficient implementations and scale thinking//Leadership Principles embedded in behavioral questions

How It Works

Master Amazon Coding Interviews

Leetcode-style problems scored on algorithm correctness and implementation efficiency. · Click to expand

1Your Coding Goals

Algorithm Design

Practitioner

Data Structure Mastery

Practitioner

2Coding Case Prep

Find the maximum product of any subarray. Handle edge cases with negative numbers.

LEARNMedium

Amazon

3Frameworks

Framework

Is time complexity the bottleneck?

Are you sorting more times than necessary?

Current: O(n log n) sort on all 50M events

With heap: O(n log k) where k=top 100 events, 2.1s vs 12s

Are lookups in the query linear scan?

Linear scan of 50M events per request: 400ms per query

Hash table index: 2ms lookup, trade 8GB memory vs 400ms latency

Is space complexity acceptable?

Is caching missing intermediate results?

Recalculate top 100 for every region: 8 regions × 50M events

Materialized view with 10-minute TTL: 800 cached results, 95% hit rate

Are data structures bloated?

Full event object in memory: 2KB per event × 50M = 100GB footprint

Store only ID + timestamp: 48 bytes per event = 2.4GB, sorted index

Are there correctness gaps for edge cases?

Does the algorithm handle deleted events?

Naive: delete event from timeline, breaks iteration on live reads (race)

Soft delete with version vector: O(1) mark, consistency preserved

Are concurrent writes handled?

Two threads insert same event ID: duplicate in result

4Feedback

Algorithm Correctness

Distinctive

Complexity Analysis

Strong

Code Clarity

Meeting Bar

Optimization Mindset

Strong

Repeat

Practice Library

Amazon exercises

Implement a function to find all permutations of a string. Optimize for time and space.

LEARNMedium

Technology · BacktrackingAmazon

Given a binary tree, find the maximum path sum. Return the maximum sum of any path in...

CASE INTERVIEWHard

Technology · Tree/RecursionAmazon

Implement the longest increasing subsequence algorithm. Explain both O(n²) and O(n log...

CASE INTERVIEWHard

Technology · Dynamic ProgrammingAmazon

Describe a coding problem where you had to handle edge cases carefully.

BEHAVIORALMedium

Behavioral · Attention to DetailAmazon

Tell me about a time you debugged a complex algorithm issue.

BEHAVIORALMedium

Behavioral · Problem SolvingAmazon