I recently went through the Amazon SDE II interview loop. Four rounds, roughly one hour each, all in one day. This post is a detailed breakdown of what was asked, how it was structured, and what I'd do differently if I had to do it again.

The Structure Before You Start

Amazon's loop is predictable in format but demanding in execution. Every round carries two weights equally: the technical problem and the Leadership Principle (LP) evaluation. You are being assessed on both simultaneously, and interviewers calibrate their hiring recommendation against specific LPs they're assigned to probe.

The LPs in scope for my loop:

  • Round 1 — Learn and Be Curious, Ownership
  • Round 2 — Earn Trust
  • Round 3 — Dive Deep
  • Round 4 (Bar Raiser) — Deliver Results, Customer Obsession, Insist on Highest Standards

Know your assigned LPs per round before you walk in. Your behavioral examples should be chosen to directly surface those qualities — not just good stories in general.

Round 1: DSA + Leadership Principles

Technical: Two algorithmic questions.

The first could be solved using multi-source BFS. Think of problems where you're expanding from multiple starting nodes simultaneously — shortest path from multiple sources, infection spread, or island flooding variants. The key insight for multi-source BFS is initializing the queue with all sources at once before starting traversal, rather than running BFS per source independently.

The second was a logic-based arrays problem. This category tests pattern recognition and the ability to reduce a problem to its core invariant. Sorting, prefix sums, two pointers, and frequency maps are your usual toolkit here.

What the interviewer is really evaluating: Can you think out loud clearly? Do you verify edge cases without being prompted? Do you get stuck and ask for help, or do you methodically decompose?

LP focus — Learn and Be Curious + Ownership: Come prepared with stories where you proactively went beyond your scope to understand something — not because you were asked to, but because it mattered. Ownership here means you saw a problem, you didn't wait for someone to assign it to you, and you drove it to completion.

Round 2: HLD + GenAI Fluency + Leadership Principles

Technical: Design a cache system like Redis, scoped to simpler data structures for the round.

This is a classic system design question with real depth if you explore it right. The progression I'd follow:

  1. Clarify requirements — read-heavy or write-heavy? TTL support? Eviction policy? Single node or distributed?
  2. Core data structure — a hash map for O(1) get/set is the baseline. Pair it with a doubly linked list for LRU eviction and you have the classic LRU cache design.
  3. Eviction policies — LRU (most common), LFU (tracks frequency, harder to implement), FIFO. Discuss tradeoffs.
  4. Persistence — Redis supports RDB snapshots and AOF (append-only file). Where does your design land on the durability vs. performance spectrum?
  5. Concurrency — how do you handle simultaneous reads and writes? Read-write locks, single-threaded event loop (Redis's actual approach), or optimistic locking?
  6. Scaling — consistent hashing for distributed cache, replication for read scaling, sentinel or cluster mode for HA.

The interviewer doesn't expect a full Redis implementation. They want to see that you understand the tradeoffs at each layer and can articulate why you're making each decision.

GenAI fluency: This was a genuine discussion — not a trivia round. Expect questions like: Have you used GenAI tools to improve your or your team's productivity? Have you solved a real business problem with AI, and what was the measurable impact? The bar here is specificity. "I use Copilot for autocomplete" is table stakes. What they're looking for is whether you've thought about AI as a force multiplier — designing systems around it, instrumenting its reliability, using it to expand your team's leverage, not just your own speed.

LP focus — Earn Trust: This is about transparency, honesty, and follow-through. Stories where you delivered bad news early, admitted a mistake and corrected course, or pushed back on a decision you disagreed with and eventually aligned — these are the ones that land here.

Round 3: LLD + Leadership Principles

Technical: Design an Amazon shopping cart — items, inventory, cart management, coupons. Payment was explicitly out of scope.

LLD rounds test your ability to model a real-world domain cleanly. Here's the structure I'd follow:

Requirement Clarifications First (non-negotiable):

  • Can a user have multiple carts (saved for later)?
  • What are the consistency requirements for inventory — can two users add the last item simultaneously?
  • What coupon types exist — percentage off, flat discount, item-specific, cart-wide?
  • Do coupons stack? Are they single-use or reusable?

Core Entities:

  • User → owns a Cart
  • Cart → has many CartItems
  • CartItem → references Product, holds quantity and price snapshot
  • Product → references Inventory
  • Inventory → tracks available stock per product
  • Coupon → has type, value, constraints (min order value, applicable products), usage count

The concurrency problem is the crux of this round. Two users adding the last unit of a product — what happens? Options:

  • Pessimistic locking — lock the inventory row on add-to-cart. Safe, but high contention.
  • Optimistic locking with versioning — read the inventory version, attempt the decrement, fail if the version changed. Better throughput, requires retry logic.
  • Reserve-then-confirm — reserve stock at add-to-cart (soft hold with TTL), confirm at checkout. This is closer to what real e-commerce systems do. Prevents the "added to cart, gone at checkout" problem.

Coupon application: Apply coupons last, after computing subtotal. Validate constraints server-side always — never trust the client's computed discount. Handle expired coupons gracefully.

LP focus — Dive Deep: This principle is about going below the surface. Don't just model the happy path. What happens when a coupon is applied to a cart that's then modified? What happens to a reserved item if the user abandons the cart? These are the questions that show you think in depth.

Round 4: Bar Raiser

The Bar Raiser is the most important round and the most misunderstood one.

It's conducted by a senior engineer from a completely different team — someone with no stake in hiring for your specific role. Their job is to ensure the hire raises the bar for Amazon as a whole, not just fills a headcount slot. They read all the feedback from your previous rounds before the interview.

Mine was entirely behavioral — five to six questions about past work, each probing a different dimension.

What they're actually evaluating:

  • Deliver Results — did you ship things that mattered, on time, despite obstacles? Not effort, not intent — outcomes.
  • Customer Obsession — do you think about the end user even when they're not in the room? Do your technical decisions trace back to user impact?
  • Insist on Highest Standards — do you hold a high bar for your own work and the work around you? Did you push back when something wasn't right?

The format that matters:

STAR is necessary but not sufficient. Amazon expects stories that are:

  1. Strategic in nature — not "I fixed a bug," but "I identified a systemic problem and changed how the team operated"
  2. Data-driven — quantify everything you can. Time saved, error rates reduced, users impacted, regressions prevented
  3. 7-8 minutes in depth — they will go deep with follow-ups. Your story needs layers, not just a surface narrative
  4. Unique per round — interviewers debrief together. Do not repeat examples across rounds

The questions I got centered around: how I navigated ambiguous problems with no clear owner, how I identified and drove improvements no one asked me to, and moments where I disagreed with a direction and how I handled it.

What I'd Tell My Past Self

On DSA: Practice problems where the insight isn't obvious — multi-source graph problems, problems that look like one pattern but resolve into another. The ability to recognize the structure underneath a problem description is what separates a solid solve from a shaky one.

On system design: Clarify before designing. Every interviewer I spoke with flagged this — a candidate who charges straight into a solution without scoping the problem is a red flag at senior level. Take two to three minutes, ask the right questions, then design.

On LLD: Concurrency is almost always the hardest part and the most probed. Know how to model it — locks, versions, soft holds, idempotency keys — and be able to articulate which tradeoff you're making and why.

On behavioral: Have two strong, distinct stories per LP. Not vague ones — stories where you can name the metrics, name the decision points, and defend the choices you made under follow-up. Ambiguity, tradeoffs, and proactive ownership are the themes Amazon keeps returning to.

On the Bar Raiser: Don't prepare for it differently. If you've done your work honestly and can talk about it with depth and specificity, the Bar Raiser round is just another honest conversation about what you've built.

Each round was genuinely challenging — not in a gotcha way, but in a "we want to see how you actually think" way. Whatever the outcome, it's a well-structured process that forces you to articulate your work clearly, which is useful regardless.

Good luck if you're preparing for yours.