72-Hour Last-Minute Cram Guide: Leadership Principles & Top Tagged Coding Questions
Amazon interviews emphasize both algorithmic problem solving and strict adherence to the 14 Leadership Principles (LPs). Every single coding interviewer dedicates 15–20 minutes specifically to behavioral LP questions. The Bar Raiser has veto power across the entire loop.
Amazon Golden Rules for Passing
- Allocate exactly 15 minutes per round to LP: Always structure answers with Situation, Task, Action (70% of words), Result (metrics).
- Data structures of choice: Heaps (PriorityQueue) and HashMaps dominate 65% of Amazon coding questions.
- Always clarify scale: Ask "Is this for a single AWS region or global distribution?" and "What is the read-to-write ratio?".
- Never blame teammates: Frame challenges as process or technical bottlenecks that YOU actively stepped up to resolve.
Typical Amazon Virtual Onsite Loop Structure
Round 1: Coding + LP
1 DSA Medium + 2 LP questions (Customer Obsession, Ownership)
Round 2: Coding + LP
1 DSA Medium/Hard + 2 LP questions (Deep Dive, Bias for Action)
Round 3: System Design + LP
High-Level Distributed Design + 2 LP questions (Deliver Results, Think Big)
Round 4: Bar Raiser
Cross-functional evaluation, deepest behavioral probes + DSA/System Design
Day 1: Top Tagged Problems & Frequency Patterns
Amazon re-uses canonical algorithmic patterns. Focus strictly on these high-yield problems today:
- Review Two Pointers & Sliding Window syntax in your primary language
- Practice 4 BFS/DFS graph problems (Number of Islands is an Amazon staple)
- Review Top K Frequent Elements and PriorityQueue operations
- Do NOT look at solutions before spending 15 timed minutes planning the approach
Priority Tagged Questions to Solve Today:
| Problem | Difficulty | Frequency | Key Strategy | Action |
|---|---|---|---|---|
| Number of Islands | Medium | 95% (Top Amazon Question) | Be ready to modify the grid in-place to save O(m*n) space or use a visited set. | |
| Course Schedule II | Medium | 85% | Track indegrees and use a queue. Explain how it relates to AWS service deployment dependencies. | |
| Top K Frequent Elements | Medium | 88% | Use a Min-Heap of size K for O(N log K) time or Quickselect for O(N) average. | |
| Merge Intervals | Medium | 80% | Sort by start time first. Essential for warehouse logistics & reservation systems. | |
| LRU Cache | Medium | 92% | Doubly Linked List + HashMap. Amazon loves asking this as a warm-up. | |
| Word Break | Medium | 78% | 1D DP array. Optimize substring search with Trie or HashSet. |
Day 2: System Design & AWS Distributed Fundamentals
Amazon favors pragmatic, highly available distributed architectures. Expect questions around high write volume and asynchronous processing.
Core Tech Stack & Patterns Tested
- Event-Driven Architecture: SQS queues, SNS notifications, Kinesis streaming
- DynamoDB vs Relational: Single-table design, partition keys, global secondary indexes
- Caching layers: ElastiCache (Redis) write-through vs cache-aside
- Fault tolerance: Dead Letter Queues (DLQ), idempotency keys, exponential backoff with jitter
Day 2 Checklist
- Practice drawing architectures top-to-bottom: Client -> CDN -> API Gateway -> Service -> Cache -> DB
- Memorize back-of-the-envelope calculations (1 million daily active users = ~12 requests/sec average)
- State trade-offs out loud: "We choose Eventual Consistency here because availability matters more than strict consistency for browsing catalogs."
Canonical Amazon Case Studies:
Design an E-Commerce Order Fulfillment & Inventory System
Prevent overselling with distributed locking (Redis Redlock) and optimistic concurrency control.
Design Amazon Locker / Delivery Tracking
Geohash spatial indexing, event notification fanout, and offline locker synchronization.
Design API Rate Limiter
Token bucket algorithm implemented with Redis Lua scripts for atomic updates.
Read Architecture SpecDay 3: Leadership Principles Matrix & Morning-Of Sprint
Prepare 5 versatile STAR stories that can pivot to any of the 14 Leadership Principles.
3 High-Impact STAR Stories to Prepare Today
"Tell me about a time you had to make a decision with incomplete data (Bias for Action)"
Model Response Template: Faced with conflicting API error logs during a prod outage, rolled back the release based on initial 5xx spike rather than waiting 2 hours for RCA.
"Tell me about a time you strongly disagreed with a team decision (Have Backbone; Disagree and Commit)"
Model Response Template: Advocated for PostgreSQL over MongoDB due to relational ACID requirements; brought concrete benchmark data, but when team voted Mongo, ensured robust schema validation.
"Give an example of when you went beyond your role (Ownership)"
Model Response Template: Noticed onboarding documentation was outdated costing new hires 3 days; rewrote setup automation scripts on my own initiative.
Morning-Of Interview Checklist
- Have a clean sheet of paper & pen ready for scratch thoughts
- Open your IDE / whiteboard link 10 minutes early; test microphone & camera
- Keep your 5 STAR stories bulleted on a sticky note near the screen
- Take 3 deep breaths: remember that interviewers want you to succeed
Sharp Questions to Ask Your Interviewer
- How does this team balance fast feature shipping with long-term architectural debt?
- What does a successful first 90 days look like for an SDE II in this organization?
- How does the team handle operational load (on-call rotation and tickets)?
Frequently Asked Questions About Amazon Interviews
Can I pass an Amazon coding interview if I do not finish all test cases?
Yes. Amazon evaluates your thought process, clean modular coding, edge-case discussion, and LP responses. A candidate who finishes 85% of clean code with excellent communication often beats a quiet coder who hacks a messy answer.
What is the role of the Bar Raiser?
The Bar Raiser is an interviewer from an unrelated team trained to ensure the candidate is better than 50% of current Amazonians at that level. They specifically probe LPs and long-term culture fit.
Can I use built-in language libraries like Python heapq or C++ std::priority_queue?
Yes! Amazon encourages using standard language libraries. Just explain their underlying time and space complexities (e.g. O(log k) push/pop).