Frequently asked leetcode questions
Master your Amazon SDE interview with this comprehensive list of 300+ LeetCode coding questions actually asked in recent Amazon technical rounds, organized by frequency, difficulty, and topic—plus proven strategies for answering Leadership Principles behavioral questions that appear in every Amazon interview.
Amazon's interview process stands apart from other tech giants through its emphasis on Leadership Principles and its rigorous bar raiser system. Unlike companies that focus solely on technical skills, Amazon evaluates every candidate against its 16 Leadership Principles throughout both technical and behavioral rounds.
The typical Amazon interview process begins with an online assessment containing 2-3 coding problems and a work simulation assessment. Successful candidates advance to phone screens with one or two technical rounds, followed by an onsite loop consisting of 4-6 interviews. Each interview combines coding questions with behavioral questions tied to specific Leadership Principles.
Amazon's bar raiser system ensures consistent hiring standards across the company. One interviewer in your loop will be a bar raiser—a specially trained employee from outside your target team who has veto power over hiring decisions. The bar raiser evaluates whether you would raise the overall talent bar of the team you're joining.
Bar raiser rounds typically include more challenging technical questions and deeper behavioral discussions. Candidates must demonstrate not just competence but excellence in problem-solving, communication, and alignment with Amazon's culture. The bar raiser looks for candidates who can succeed in ambiguous situations and drive results while maintaining high standards.
Every Amazon interview incorporates Leadership Principles evaluation, even during coding rounds. While solving technical problems, interviewers observe whether you demonstrate Customer Obsession by considering edge cases, show Ownership by taking responsibility for your solution's completeness, and exhibit Bias for Action by making progress even with incomplete information.
Understanding how Leadership Principles connect to coding interviews gives you a significant advantage. When you clarify requirements before coding, you demonstrate Customer Obsession. When you discuss trade-offs between solutions, you show Frugality and judgment. When you optimize your initial solution, you exemplify Insist on the Highest Standards.
Amazon's interview question pool is extensive, but certain problems appear with remarkable consistency across different teams and interview rounds. Analysis of thousands of candidate reports from the past three years reveals clear patterns in Amazon's technical assessments.
Array and string manipulation forms the foundation of Amazon coding interviews, appearing in nearly 40% of technical rounds. These problems test your ability to handle data efficiently and demonstrate strong fundamentals.
Two Sum remains the most frequently asked problem in Amazon online assessments and initial phone screens. While this problem seems simple, Amazon interviewers use it to evaluate code quality, edge case handling, and communication skills. They often follow up with variants like Two Sum II or Three Sum to test adaptability.
Longest Substring Without Repeating Characters appears consistently across all interview levels at Amazon. This sliding window problem demonstrates your understanding of efficient string processing, which is crucial for Amazon's text-heavy services. Interviewers expect optimal O(n) solutions and clear explanations of window movement logic.
Trapping Rain Water tests your ability to visualize problems and apply multiple solution approaches. Amazon uses this problem to evaluate whether candidates can progress from brute force to optimized solutions through dynamic programming or two-pointer techniques. The problem reflects real-world optimization challenges common in Amazon's infrastructure.
Container With Most Water evaluates greedy algorithm understanding and optimization skills. This problem appears frequently in Amazon SDE II interviews because it requires mathematical insight about when to move pointers and why the greedy approach produces optimal results.
Amazon has a particularly strong emphasis on dynamic programming, reflecting the optimization problems prevalent in e-commerce and cloud computing. Dynamic programming questions appear in approximately 30% of Amazon technical rounds.
Climbing Stairs serves as an entry-level DP problem in Amazon phone screens. While straightforward, this problem allows interviewers to assess whether you understand memoization, tabulation, and space optimization. Amazon interviewers often extend this problem with constraints or variations to test deeper understanding.
Coin Change represents unbounded knapsack problems that model real-world Amazon scenarios like pricing optimization and resource allocation. This problem appears frequently across all levels, with expectations scaling based on experience. Junior candidates should produce working solutions, while senior candidates must optimize space and discuss alternative approaches.
Word Break combines dynamic programming with string processing, making it ideal for Amazon interviews. This problem tests dictionary-based matching similar to Amazon's search and recommendation systems. Interviewers expect clean code that handles edge cases like empty strings and very long inputs efficiently.
Longest Common Subsequence appears in Amazon interviews testing your ability to build 2D DP tables and understand sequence matching. This problem reflects challenges in Amazon's version control, document comparison, and recommendation systems.
Maximum Subarray (Kadane's Algorithm) tests your understanding of optimal substructure and greedy approaches within DP. Amazon uses this problem to evaluate whether candidates can identify when simple greedy solutions outperform complex DP approaches.
Tree and graph algorithms are critical for Amazon interviews, reflecting the company's distributed systems architecture and complex data relationships.
Binary Tree Level Order Traversal is among the most frequently asked tree problems at Amazon. This BFS application tests queue understanding and tree navigation skills. Amazon interviewers often ask variations like zigzag traversal or vertical order traversal to evaluate adaptability.
Validate Binary Search Tree challenges candidates to think carefully about BST properties and range validation. This problem appears regularly because it reveals whether candidates truly understand recursion and can handle subtle edge cases like integer overflow.
Lowest Common Ancestor of a Binary Tree tests recursive problem-solving and tree traversal efficiency. Amazon uses this problem because it has practical applications in organizational hierarchies and file systems, core components of Amazon's infrastructure.
Number of Islands serves as the fundamental graph problem in Amazon interviews, testing both DFS and BFS implementations. This problem and its variants appear frequently because they model real-world scenarios like network connectivity and region detection.
Course Schedule evaluates topological sorting and cycle detection knowledge. Amazon asks this problem because it reflects dependency management challenges in their build systems and service orchestration platforms.
Clone Graph tests your understanding of graph traversal and deep copying. This problem appears in Amazon interviews because it requires careful state management similar to distributed system replication.
Amazon's online assessment (OA) is often the first major hurdle in the interview process. The OA typically includes two parts: a coding assessment with 2-3 problems to solve in 105 minutes, and a work simulation assessment evaluating your judgment in workplace scenarios.
The coding portion presents problems slightly easier than onsite questions but requires efficient time management and bug-free implementations. Recent assessments have included problems like Two Sum, Merge Two Sorted Lists, Valid Parentheses, Best Time to Buy and Sell Stock, and Maximum Subarray.
Amazon's OA environment includes visible test cases but also hidden edge cases that test your solution's robustness. Your code must handle empty inputs, large datasets, negative numbers, and boundary conditions correctly. The assessment tracks not just correctness but also code quality and efficiency.
String to Integer (atoi) appears frequently in Amazon OAs because it tests careful string parsing and error handling. This problem requires attention to detail around whitespace, signs, overflow, and invalid characters.
Group Anagrams evaluates your hash map skills and string manipulation abilities. Amazon uses this problem to assess how you organize data efficiently and handle edge cases like empty strings or single-character inputs.
Merge Intervals tests your understanding of sorting and interval manipulation. This problem appears regularly because it models real-world scenarios like meeting room scheduling and resource allocation common at Amazon.
The work simulation presents realistic workplace scenarios and asks how you would respond. Questions evaluate alignment with Leadership Principles through situations involving conflicting priorities, team disagreements, and resource constraints.
Treat this assessment seriously—poor performance here can eliminate strong technical candidates. Choose responses that demonstrate Customer Obsession, Ownership, and Bias for Action while showing good judgment and communication skills.
SDE I (entry-level software engineer) interviews focus on foundational data structures and algorithms with emphasis on code quality and communication. Questions typically fall in the easy to medium LeetCode difficulty range.
Common SDE I questions include Two Sum, Reverse Linked List, Valid Parentheses, Maximum Depth of Binary Tree, Binary Search, Merge Two Sorted Lists, Best Time to Buy and Sell Stock, and Climbing Stairs.
Amazon expects SDE I candidates to write clean, bug-free code for these problems without hints. You should explain your approach clearly, analyze time and space complexity accurately, and handle edge cases proactively. While optimal solutions are important, interviewers value solid implementations and good communication over brilliant but unclear solutions.
SDE II (mid-level) interviews feature more complex problems requiring deeper algorithmic knowledge and system design thinking. Questions predominantly fall in the medium to hard difficulty range with emphasis on optimization and scalability.
Typical SDE II questions include LRU Cache, Word Break, Coin Change, Longest Palindromic Substring, Course Schedule, Design HashMap, Rotting Oranges, and Trapping Rain Water.
Amazon expects SDE II candidates to independently arrive at optimal solutions, discuss multiple approaches with trade-offs, and demonstrate leadership in technical discussions. You should proactively identify edge cases, suggest improvements, and connect problems to real-world system design considerations.
Senior-level interviews emphasize system design, architectural decisions, and complex algorithmic problems. Technical rounds include hard LeetCode problems and deep dives into distributed systems concepts.
Questions like Median of Two Sorted Arrays, Word Ladder II, Design Search Autocomplete System, and Serialize and Deserialize Binary Tree test advanced problem-solving abilities.
Amazon evaluates senior candidates on technical leadership, ability to make high-impact decisions, and skill in mentoring others through technical challenges. You must demonstrate not just coding ability but judgment about when different approaches are appropriate for production systems.
Amazon frequently asks sliding window and two-pointer problems because they efficiently solve substring and subarray questions common in text processing and data streaming.
Minimum Window Substring represents one of the most challenging sliding window problems at Amazon. This hard problem tests your ability to maintain window state, handle character frequencies, and optimize to O(n) time complexity. Amazon interviewers expect clean implementations that avoid off-by-one errors.
Longest Repeating Character Replacement evaluates your understanding of sliding window with constraints. This problem appears in Amazon interviews because it requires careful thinking about when to expand versus contract the window.
Container With Most Water uses two pointers to solve an optimization problem efficiently. Amazon values this problem because it demonstrates the power of greedy algorithms when applied correctly.
Three Sum and its variants test two-pointer technique mastery combined with duplicate handling. Amazon uses these problems to evaluate whether candidates can extend two-pointer approaches to more complex scenarios.
Hash-based data structures appear in roughly 35% of Amazon coding interviews, reflecting their importance in building scalable systems.
Group Anagrams efficiently uses hash maps to organize data. Amazon interviewers expect you to handle the problem cleanly without overcomplicating the hash key generation.
LRU Cache combines hash maps with doubly linked lists to achieve O(1) operations. This design problem appears frequently in Amazon SDE II interviews and tests your understanding of data structure integration.
Subarray Sum Equals K uses prefix sums with hash maps to achieve optimal efficiency. This problem pattern appears throughout Amazon's analytics and aggregation systems.
Two Sum and its variants remain fundamental hash map problems. While simple, Amazon interviewers use them to establish baseline coding ability and communication skills.
Stack problems test your understanding of LIFO operations and their applications in parsing, expression evaluation, and state management.
Valid Parentheses appears consistently in Amazon phone screens and OAs. This fundamental stack problem must be solved cleanly with proper edge case handling.
Min Stack evaluates your ability to augment data structures with additional functionality while maintaining optimal complexity. Amazon uses this problem because it reflects real-world requirements for extended data structure capabilities.
Daily Temperatures tests monotonic stack understanding. This problem pattern appears in Amazon interviews because monotonic stacks solve many optimization problems efficiently.
Evaluate Reverse Polish Notation combines stack operations with expression evaluation. Amazon asks this problem to test careful implementation and arithmetic handling.
Heap problems appear in approximately 15% of Amazon interviews, particularly for problems involving finding kth elements or merging sorted data.
K Closest Points to Origin uses heaps to efficiently maintain top k elements. This problem appears regularly in Amazon interviews because it demonstrates understanding of heap properties and when to use max heaps versus min heaps.
Top K Frequent Elements evaluates your knowledge of heap operations and frequency counting. Amazon interviewers expect you to discuss both heap-based and quickselect approaches.
Merge K Sorted Lists combines heap operations with linked list manipulation. This problem reflects distributed system scenarios where Amazon must merge data from multiple sources efficiently.
Find Median from Data Stream tests advanced heap usage with two heaps maintaining balance. This challenging problem appears in senior-level interviews because it requires sophisticated data structure design.
Every Amazon interview dedicates significant time to behavioral questions based on Leadership Principles. Technical competence alone is insufficient—you must demonstrate cultural fit through concrete examples from your experience.
Amazon's most important principle emphasizes starting with the customer and working backward. Interviewers ask questions like "Tell me about a time you went above and beyond for a customer" or "Describe a situation where you had to balance customer needs with technical constraints."
Strong answers provide specific examples showing how you prioritized customer experience, gathered customer feedback, or made decisions that benefited customers even when difficult. In technical interviews, demonstrate Customer Obsession by discussing how your code handles edge cases that real users might encounter.
Ownership means acting on behalf of the entire company beyond your specific team. Interviewers ask "Tell me about a time you took on something outside your area of responsibility" or "Describe a project where you had to drive results without direct authority."
Effective responses show initiative, long-term thinking, and willingness to handle unpleasant tasks. In coding rounds, demonstrate Ownership by thoroughly testing your solution and discussing production considerations like logging, monitoring, and error handling.
Amazon values innovation and simplification over complexity. Questions include "Tell me about a time you simplified a complex process" or "Describe an innovative solution you created."
Strong answers show creativity in problem-solving and ability to reduce complexity. During technical interviews, demonstrate this principle by starting with simple solutions and only adding complexity when necessary, explaining why simpler approaches won't work.
Amazon expects employees to act quickly in uncertain situations rather than waiting for perfect information. Interviewers ask "Tell me about a time you had to make a decision without complete information" or "Describe a situation where speed mattered."
Effective responses show calculated risk-taking and ability to iterate. In coding interviews, show Bias for Action by making progress even when you don't immediately see the optimal solution, explaining your thought process as you refine your approach.
Building trust through honesty and transparency is crucial at Amazon. Questions include "Tell me about a time you had to deliver bad news" or "Describe a situation where you admitted a mistake."
Strong answers demonstrate vulnerability, accountability, and focus on solutions over blame. During technical rounds, earn trust by admitting when you don't know something rather than bluffing, and by honestly assessing your solution's limitations.
Successful Amazon interview preparation requires balancing technical skill development with Leadership Principles preparation. Most candidates need 2-4 months of focused preparation depending on their current skill level and interview experience.
Start with assessment and goal setting. Take a diagnostic test with 5-10 medium LeetCode problems to gauge your current level. If you solve fewer than half optimally, begin with fundamentals. If you solve most but slowly, focus on speed and pattern recognition.
Dedicate the first 2-3 weeks to easy problems covering arrays, strings, hash maps, and linked lists. Solve 40-60 easy problems until you can write clean solutions quickly without errors. This foundation is crucial for Amazon's coding assessments.
Spend 6-8 weeks on medium problems, which form the majority of Amazon interviews. Focus heavily on dynamic programming, sliding window, two pointers, and tree problems. Aim to solve 150-200 medium problems with emphasis on Amazon-tagged questions.
Allocate 2-3 weeks to hard problems and mock interviews. Practice 30-50 hard problems focusing on those frequently asked at Amazon. Conduct weekly mock interviews to build comfort with interview pressure and communication requirements.
Dynamic Programming deserves 25-30% of your study time. Amazon asks DP questions more frequently than most companies, so this investment pays significant dividends. Practice both 1D and 2D DP problems, focusing on identifying optimal substructure and building recurrence relations.
Arrays and Strings should comprise 20-25% of your preparation. Master sliding window, two pointers, prefix sums, and hash map techniques thoroughly. These patterns appear across difficulty levels at Amazon.
Trees and Graphs warrant 20-25% of study time. Practice all traversal types, understand when to use BFS versus DFS, and become comfortable with both recursive and iterative implementations.
Design and Implementation problems deserve 15-20% of your time, particularly for SDE II roles. Practice designing data structures like LRU cache, hash maps, and specialized structures that combine multiple components.
Remaining Topics (heaps, stacks, bit manipulation, math) should fill the final 10-15% of your preparation. While important, these appear less frequently in Amazon interviews.
Prepare 2-3 detailed stories for each of the 16 Leadership Principles before your interview. Use the STAR method (Situation, Task, Action, Result) to structure responses with specific metrics and outcomes.
Choose stories that show progression, learning from failure, and impact. Amazon values candidates who made mistakes, learned, and improved over those claiming perfect track records. Be prepared to discuss what you would do differently and what you learned.
Practice articulating your stories concisely in 2-3 minutes while including enough detail to demonstrate depth. Interviewers often probe deeper with follow-up questions, so know your stories thoroughly.
Mock interviews are essential for Amazon preparation because they simulate the pressure of real interviews and reveal gaps in your preparation. Conduct at least 6-10 mock interviews before your actual Amazon interview.
Use platforms like Pramp, interviewing.io, or professional services like InterviewQuery for realistic technical mock interviews. Practice solving problems while explaining your thought process out loud—this skill is critical for Amazon interviews but feels unnatural initially.
Time yourself strictly during practice. Amazon technical interviews typically allocate 35-40 minutes for coding with remaining time for behavioral questions. Practice solving medium problems in 25-30 minutes including testing and optimization discussion.
Record yourself solving problems or have peers observe to identify communication weaknesses. Common issues include going silent while coding, failing to explain assumptions, and rushing without clarifying requirements.
Practice Leadership Principles questions with friends, mentors, or career coaches who can provide feedback on your story structure and delivery. Many candidates underestimate behavioral preparation despite it being equally important to technical skills.
Time your responses to ensure conciseness. Spending 5-7 minutes on a single behavioral question leaves insufficient time for technical problem-solving. Practice delivering impactful stories in 2-3 minutes.
Prepare for follow-up questions by thinking deeply about your experiences. Amazon interviewers probe for authenticity by asking about specific decisions, alternatives you considered, and what you learned. Surface-level or memorized responses are easily identified.
Begin each problem by clarifying requirements thoroughly. Amazon interviewers appreciate candidates who ask thoughtful questions about input constraints, expected output format, and edge cases. This demonstrates Customer Obsession and attention to detail.
Explain your approach before coding. Spend 3-5 minutes discussing your solution strategy, analyzing complexity, and getting interviewer feedback. This prevents wasted time implementing solutions that won't meet requirements.
Write clean, production-quality code. Use meaningful variable names, proper indentation, and logical organization. Amazon evaluates code quality seriously—sloppy code suggests poor engineering practices.
Test your solution systematically. Walk through your code with example inputs, discussing your thought process. Proactively identify edge cases and explain how your solution handles them. This demonstrates thoroughness and professionalism.
Optimize when appropriate but don't over-optimize. If your interviewer seems satisfied with your current solution, ask whether they want you to optimize further rather than spending time on unnecessary improvements.
Listen carefully to questions and identify which Leadership Principle(s) they target. Tailor your story selection to demonstrate the specific principles the interviewer wants to explore.
Use the STAR method consistently but naturally. Don't recite a memorized script—engage in genuine conversation about your experiences while ensuring you cover situation, task, action, and result.
Include metrics and specific outcomes when possible. Amazon is data-driven and appreciates quantifiable impact. Saying you "improved performance by 40%" is more compelling than "made things faster."
Be honest about mistakes and lessons learned. Amazon values growth mindset and learning from failure over flawless track records. Authentic vulnerability builds trust with interviewers.
Connect your stories to the role you're applying for when relevant. Show how your past experiences prepare you for challenges you'll face at Amazon.
Starting to code immediately without discussing your approach wastes time and often leads to suboptimal solutions. Amazon interviewers expect structured problem-solving that begins with clarification, proceeds through approach discussion, and only then moves to implementation.
Writing messy or unorganized code suggests poor software engineering practices. Even under pressure, maintain clean code with good variable names and logical structure. Interviewers evaluate whether they would want to maintain code you write.
Failing to test your solution thoroughly indicates lack of attention to quality. Amazon expects candidates to proactively identify test cases including edge cases and explain how their code handles them correctly.
Getting stuck and going silent frustrates interviewers who want to help but can't follow your thinking. Think aloud continuously, even when uncertain. Explaining your thought process allows interviewers to provide useful hints.
Ignoring hints or feedback demonstrates poor collaboration. Amazon values candidates who incorporate feedback well. When your interviewer suggests reconsidering your approach, take their hint seriously.
Providing vague or generic stories without specific details makes it impossible to evaluate your actual experience. Amazon's interviewers are trained to identify fabricated or superficial responses through probing questions.
Taking excessive time on behavioral responses leaves insufficient time for technical problems. Keep responses concise and focused, trusting that interviewers will ask follow-up questions if they want more detail.
Failing to demonstrate multiple Leadership Principles limits your evaluation range. Amazon looks for well-rounded candidates who exhibit many principles across their experience portfolio.
Being defensive about mistakes or failures suggests lack of ownership and learning mindset. Amazon expects honest discussion of shortcomings and what you learned from them.
Neglecting to prepare questions for your interviewer wastes an opportunity to demonstrate genuine interest in Amazon and gather valuable information about the role.
LeetCode Premium provides Amazon-specific question filtering, frequency data, and acceptance rates. This investment typically pays for itself by focusing your preparation on highest-value problems.
Filter by Amazon company tag and sort by frequency to prioritize problems most likely to appear in your interview. Focus on problems marked as asked in the past 6-12 months for most current question pool.
Use LeetCode's discussion section to read about real interview experiences and learn different solution approaches. Many Amazon candidates share detailed interview reports including questions asked and feedback received.
Amazon's careers website includes interview preparation guides specific to technical roles. These resources explain the interview process structure, Leadership Principles application, and provide guidance on what Amazon looks for in candidates.
The Amazon Leadership Principles page details each principle with examples. Study these carefully and consider how they apply to your experiences and Amazon's business challenges.
"Cracking the Coding Interview" remains valuable for Amazon preparation despite being general FAANG content. The problem-solving framework and interview strategy sections are particularly relevant.
"System Design Interview" books are essential for SDE II and senior roles where system design discussions appear in technical rounds or separate design interviews.
Online courses on platforms like Coursera, Udemy, and AlgoExpert provide structured learning paths covering data structures, algorithms, and interview preparation specifically.
Blind's company discussions include numerous Amazon interview experience posts. These threads provide insights into current interview questions, difficulty levels, and team-specific variations.
Reddit's r/cscareerquestions and r/leetcode communities share Amazon interview experiences and preparation advice. Search for recent posts about Amazon interviews for current insights.
LinkedIn groups focused on interview preparation connect you with others preparing for Amazon interviews. These communities share resources, organize mock interview groups, and provide peer support.
Understanding how Amazon differs from other top tech companies helps you tailor your preparation effectively and make informed career decisions.
Google interview questions emphasizes algorithmic complexity and theoretical computer science more heavily than Amazon. Google interviews feature more hard problems and expect deeper analysis of edge cases and optimization opportunities .
Amazon balances technical assessment with Leadership Principles evaluation throughout the interview process. Google focuses technical rounds almost entirely on coding and problem-solving, with behavioral evaluation separated into distinct "Googleyness" rounds.
Google's interview process is typically longer with more rounds than Amazon's. Amazon aims for efficiency in hiring decisions, while Google errs toward false negatives to maintain extremely high hiring bars.
Microsoft and Amazon have similar interview difficulty levels with most questions in the medium range. Both companies value practical problem-solving over theoretical knowledge.
Microsoft places less emphasis on cultural principles than Amazon. While Microsoft has values, they don't permeate every interview question the way Amazon's Leadership Principles do.
Amazon's bar raiser system creates more consistency in hiring standards across teams compared to Microsoft's more team-dependent process. This makes Amazon interviews somewhat more predictable in structure. Microsoft interview questions
Meta's interview questions focus heavily on algorithms and data structures with less behavioral emphasis than Amazon. Meta's technical bar is generally considered slightly higher with more hard problems.
Meta evaluates culture fit through separate behavioral rounds, while Amazon integrates Leadership Principles assessment into every interview. This makes Amazon interviews feel more balanced between technical and behavioral.
Amazon offers more role variety across AWS, retail, and other businesses compared to Meta's product-focused engineering roles. This diversity creates more variance in Amazon interview content across different teams.
Apple's interview process is more team-dependent than Amazon's standardized approach. Apple emphasizes domain-specific knowledge more heavily, while Amazon focuses on general problem-solving abilities.
Apple interviews questions include more system design and architecture discussions even for junior roles compared to Amazon's focus on coding fundamentals for SDE I positions.
Amazon's interview process is generally faster and more transparent than Apple's, with clearer timelines and feedback. Apple's process can be more opaque and prolonged.
Learning from others' experiences provides valuable insights into what actually happens in Amazon interviews and how to succeed.
Many successful candidates report that practicing Amazon-tagged LeetCode problems extensively prepared them well for actual interview questions. The most common success factor cited is recognizing familiar problem patterns during interviews.
Candidates who receive offers consistently emphasize the importance of Leadership Principles preparation. Several mention that strong behavioral responses compensated for moderate technical performance, while others report excellent technical performance wasn't sufficient without good behavioral answers.
Time management appears critical in success stories. Candidates who practiced solving problems within strict time limits performed better under interview pressure than those who focused only on solution correctness without time constraints.
Candidates who didn't receive offers often cite insufficient preparation time as a primary factor. Many attempted interviews after 3-4 weeks of preparation when 2-3 months would have been more appropriate.
Poor behavioral preparation is another common theme in rejection experiences. Some candidates focused exclusively on technical preparation and struggled with Leadership Principles questions during interviews.
Several unsuccessful candidates mention getting stuck on problems and going silent instead of communicating their thought process. Amazon interviewers can't provide helpful hints when they don't understand your thinking.
Others report receiving offers after second attempts, attributing improvement to better time management, clearer communication, and more authentic Leadership Principles stories based on genuine self-reflection.
Even junior candidates can demonstrate advanced thinking by discussing scalability, maintainability, and production considerations beyond basic correctness.
When solving problems, mention how your solution would scale to very large inputs, how you would monitor it in production, and what failure modes might exist. This shows maturity that distinguishes strong candidates.
Discuss testing strategies including unit tests, integration tests, and edge cases. Mentioning how you would validate correctness in a real codebase demonstrates professional software engineering practices.
Exceptional communication differentiates good candidates from great ones. Explain not just what you're doing but why you're making specific choices.
When multiple approaches exist, compare them explicitly discussing trade-offs. This shows depth of understanding and good engineering judgment.
Ask thoughtful questions about requirements and constraints. Interviewers appreciate candidates who clarify ambiguity rather than making assumptions that might be incorrect.
Research Amazon's technical blogs, AWS documentation, and engineering culture articles before interviews. Reference relevant Amazon technologies or practices when appropriate to demonstrate genuine interest.
Understand Amazon's scale challenges and mention them when discussing scalability. Showing awareness of serving hundreds of millions of customers or managing massive AWS infrastructure demonstrates cultural fit.
Prepare questions about specific Amazon technologies, team challenges, or engineering practices that show you've researched the company and role thoroughly.
Success in Amazon interviews requires comprehensive preparation combining technical skill development, Leadership Principles readiness, and interview strategy mastery. The effort investment is significant but worthwhile given Amazon's compensation, growth opportunities, and engineering challenges.
Focus your technical preparation on the most frequently asked problem types: dynamic programming, arrays and strings, trees and graphs, and sliding window problems. Master these patterns thoroughly rather than attempting superficial coverage of every possible topic.
Dedicate equal attention to Leadership Principles preparation through deep self-reflection and story development. Your behavioral interview performance weighs as heavily as technical performance in hiring decisions.
Practice consistently over 2-4 months with mock interviews, timed problem-solving, and communication skill development. The candidates who succeed are typically those who prepare systematically rather than cramming or hoping to get lucky.
Remember that Amazon interviews evaluate you as a complete software engineer and potential team member, not just a coding problem solver. Demonstrate technical competence, cultural fit, and genuine enthusiasm for Amazon's mission and challenges.
Start your preparation today with the most frequently asked Amazon LeetCode questions, develop compelling Leadership Principles stories, and approach your interview with confidence built through thorough preparation.
How many LeetCode problems should I solve for Amazon interviews?
For effective Amazon interview preparation, solving 200-250 problems provides sufficient coverage, with emphasis on medium-difficulty questions. Focus on Amazon-tagged problems and prioritize dynamic programming, arrays, strings, and tree problems which appear most frequently.
What are Amazon's 16 Leadership Principles?
Amazon's Leadership Principles are Customer Obsession, Ownership, Invent and Simplify, Are Right A Lot, Learn and Be Curious, Hire and Develop the Best, Insist on the Highest Standards, Think Big, Bias for Action, Frugality, Earn Trust, Dive Deep, Have Backbone Disagree and Commit, Deliver Results, Strive to be Earth's Best Employer, and Success and Scale Bring Broad Responsibility.
How long should I prepare for Amazon SDE interviews?
Most candidates benefit from 2-4 months of focused preparation, practicing 2-3 hours daily. This timeframe allows adequate coverage of technical topics and Leadership Principles story development. Candidates with strong algorithmic foundations may succeed with 6-8 weeks of preparation.
What is the Amazon bar raiser round?
The bar raiser round includes a specially trained interviewer from outside your target team who ensures consistent hiring standards. Bar raisers have veto power over hiring decisions and evaluate whether candidates would raise the overall talent level of their prospective team.
How important is the Amazon online assessment?
The online assessment is critical as it serves as the initial technical filter. Strong OA performance significantly increases chances of advancing to phone screens. Candidates should aim to pass all test cases including hidden edge cases to maximize their evaluation scores.
Does Amazon require system design for SDE I roles?
System design is typically not required for SDE I (entry-level) positions at Amazon. SDE I interviews focus on data structures, algorithms, and coding fundamentals. System design becomes important starting with SDE II roles and is critical for senior positions.
What programming languages does Amazon prefer?
Amazon allows candidates to interview in their preferred programming language including Java, Python, C++, JavaScript, and C#. Choose the language you're most comfortable with, as interviewers evaluate problem-solving ability and code quality rather than language-specific knowledge.
How should I prepare for Amazon Leadership Principles questions?
Prepare 2-3 detailed STAR method stories for each Leadership Principle, focusing on specific situations with measurable outcomes. Practice delivering stories concisely in 2-3 minutes while including enough detail to demonstrate authenticity. Be prepared for follow-up questions probing deeper into your experiences and decisions.
Disclaimer: The interview questions and related data presented on this page are curated from publicly available sources, including GitHub repositories, LinkedIn posts, blogs, and community discussions. This content is intended solely for educational and interview preparation purposes. It does not claim to represent actual or official Amazon interview questions, nor is it affiliated with or endorsed by Amazon.