Prepare for your Amazon software engineering interview

Coding, system design, and leadership principle-driven behavioral rounds calibrated to Amazon's builder culture and ownership mindset.

Socratify AI

The Interview

What Amazon is looking for

Coding Interview

Algorithm & Data Structures

01Algorithm Complexity Analysis

02Data Structure Optimization

03Clean Code & Readability

04Pragmatic Trade-off Decisions

System Design Interview

System Architecture & Scale

01Distributed Systems & Consistency

02Database & Storage Optimization

03Fault Tolerance & Reliability

04Infrastructure Cost Awareness

Behavioral Interview

Leadership Principles Interview

01Ownership & Accountability

02Dive Deep & Data-Driven Decisions

03Invent & Simplify

04Bias for Action

Leadership Principles guide every hiring decision//Ownership mindset and accountability expectations//Pragmatism and delivery speed valued above perfection

Practice Library

Amazon exercises

Ready to ace your Amazon software engineering interview?

Prepare for your Amazon software engineering interview

Coding, system design, and leadership principle-driven behavioral rounds calibrated to Amazon's builder culture and ownership mindset.

Socratify AI

The Interview

What Amazon is looking for

Coding Interview

Algorithm & Data Structures

01Algorithm Complexity Analysis

02Data Structure Optimization

03Clean Code & Readability

04Pragmatic Trade-off Decisions

System Design Interview

System Architecture & Scale

01Distributed Systems & Consistency

02Database & Storage Optimization

03Fault Tolerance & Reliability

04Infrastructure Cost Awareness

Behavioral Interview

Leadership Principles Interview

01Ownership & Accountability

02Dive Deep & Data-Driven Decisions

03Invent & Simplify

04Bias for Action

Leadership Principles guide every hiring decision//Ownership mindset and accountability expectations//Pragmatism and delivery speed valued above perfection

How It Works

Master Amazon Software Engineering Interviews

Coding, system design, and leadership principle behavioral rounds scored on Amazon's evaluation dimensions. · Click to expand

System Design

Practitioner

Algorithm Design

Practitioner

1Your Software Goals

2Software Case Prep

Given an input stream of numbers, design an algorithm to find the k largest numbers...

LEARNMedium

Technology · Algorithm DesignAmazon

3Frameworks

Framework

Is the query execution path the primary bottleneck?

Is the ranking model adding disproportionate computation time?

Feature computation for 500 results: 620ms avg — 73% of P99 budget consumed by ranking alone

Top-3 features use O(n²) cross-product — 500 results × 500 dimensions = 250K ops per query

Is the index scan missing an optimal access path?

Query plan: full table scan on filtered dimension — composite index missing on (category, score)

Adding composite index reduces scan from 2M rows to 1.2K rows — 1,600× selectivity improvement

Is network and serialization overhead contributing significantly?

Is the response payload larger than the client actually needs?

Response: 2.3MB uncompressed for 500 results — 97% of fields unused by frontend client

Field projection reduces payload to 48KB — 98% reduction, 140ms serialization saving per request

Is JSON serialization blocking the response thread?

Serialization on main thread: 85ms measured — async streaming available but not enabled

P50 latency drops 70ms after async serialization migration — thread blocking confirmed

Is the caching strategy ineffective for this query workload?

Is the cache hit rate too low for the observed query distribution?

Cache hit rate: 23% (target 70%) — LRU eviction removing hot items due to 10K-item tail queries

Top 200 query patterns account for 78% of traffic — LFU cache would raise hit rate to 68%

Is cache invalidation too aggressive, causing unnecessary misses?

TTL: 30s global — stable brand-name queries invalidated 120×/hour despite no underlying data change

Tiered TTL (stable: 10 min, dynamic: 30s) reduces invalidation events 85% with <0.1% staleness

Complexity Analysis

Distinctive

Distributed Systems

Strong

Technical Communication

Meeting Bar

Pragmatic Trade-offs

Strong

4Feedback

How It Works

Master Amazon Software Engineering Interviews

Coding, system design, and leadership principle behavioral rounds scored on Amazon's evaluation dimensions. · Click to expand

1Your Software Goals

System Design

Practitioner

Algorithm Design

Practitioner

2Software Case Prep

Given an input stream of numbers, design an algorithm to find the k largest numbers...

LEARNMedium

Amazon