Amazon New Grad Technical Mock Interview (SDE I)
A practical, interview-style framework to practice Amazon’s coding + Leadership Principles expectations: clarifying questions, correct and efficient solutions, and clean communication under time pressure.
What Amazon evaluates in a new grad technical mock interview
A strong Amazon new grad mock interview simulates two things at once: (1) coding performance (problem solving, data structures, correctness, complexity, test coverage) and (2) decision-making and communication aligned with Amazon’s Leadership Principles (LPs). The mental model: treat every round as a structured engineering loop—understand the requirements, propose an approach with tradeoffs, implement cleanly, validate with tests/edge cases, and summarize. Interviewers are not only checking if you can finish; they’re checking if you can reliably drive ambiguity to clarity, make sound choices, and communicate like a teammate.
A live-interview framework you can follow (45–60 minutes)
1) Restate the problem + confirm inputs/outputs
In 30–60 seconds, paraphrase the ask and confirm data types, constraints, and expected output format. Ask only high-impact questions: size limits (n), value ranges, duplicates, ordering, and whether you can mutate inputs.
2) Identify the core pattern and propose a baseline solution
Name the likely pattern (hash map, two pointers, BFS/DFS, heap, DP). Offer a straightforward solution first, even if not optimal, then state expected time/space. This shows you can get to a working approach quickly.
3) Optimize with constraints-driven reasoning
Use constraints to justify the final approach. If n is large, avoid O(n^2). If streaming, avoid full sorting. If memory is tight, consider in-place or iterative variants. Explicitly compare 1–2 alternatives and choose one.
4) Define invariants + edge cases before coding
State 2–3 invariants you’ll maintain (e.g., sliding window counts stay accurate; visited set prevents cycles). Call out edge cases: empty input, single element, all duplicates, negative values, disconnected graph, etc.
5) Implement in clean, interview-friendly steps
Write a clear function signature, pick descriptive variable names, and code in small chunks. Narrate your intent: data structures used, updates per iteration, and when you add/remove from sets/maps/queues. Avoid clever one-liners that are hard to explain.
6) Validate with targeted tests and complexity
Run through 2–3 tests out loud, including an edge case. If you find a bug, isolate and fix it methodically. End by stating final time/space complexity and why it matches constraints.
7) Tie in Leadership Principles when prompted
For behavioral questions or follow-ups, answer with a concise STAR story. Make the engineering decision explicit (tradeoff, metric, rollout, failure mode) and connect it to an LP (Customer Obsession, Ownership, Dive Deep, Bias for Action, Learn and Be Curious).
Common Amazon new grad mock interview mistakes (and why they hurt)
- •Skipping clarification and solving the wrong problem (e.g., misinterpreting “first,” mutability, or constraints).
- •Jumping into code without stating an approach, leading to scattered implementations and missed edge cases.
- •Using the right data structure but failing to explain invariants (why the algorithm is correct).
- •Not testing edge cases out loud; Amazon interviewers heavily weight validation and correctness.
- •Over-indexing on micro-optimizations (premature complexity tricks) instead of a clear, correct solution.
- •Weak complexity analysis (hand-wavy Big-O, ignoring space, or confusing average vs worst case).
- •Behavioral answers that are only ‘what I did’ without measurable impact, tradeoffs, or a clear LP connection.
- •Getting stuck and going silent; not asking for a hint or proposing a smaller subproblem to make progress.
Example: Amazon-style coding prompt and what “good” sounds like
Weak Answer
Interviewer: “Given a list of product IDs, return the first ID that appears twice.” Candidate: “I’ll just loop and check duplicates.” (Starts coding.) Code uses nested loops, no constraints discussion, forgets to handle empty list, and only tests one happy path. When asked about complexity: “Probably fine.”
Strong Answer
Interviewer: “Given a list of product IDs, return the first ID that appears twice.” Candidate: “Let me confirm: ‘first’ means the earliest second occurrence as we scan left to right, right? If there are no repeats, return -1? Also IDs fit in int?” Candidate: “Baseline is O(n^2) with nested loops, but we can do O(n) using a hash set while scanning. The first time we see an ID that’s already in the set, that’s the earliest second occurrence, so we can return immediately.” Candidate: “Edge cases: empty list, no duplicates, duplicates at positions 0/1, and multiple duplicates where the earliest second hit wins.” Candidate implements a single pass with a set, runs through tests ([], [5], [1,2,3], [2,1,3,5,3,2]) and returns 3 for the last case because its second occurrence appears before 2’s second occurrence. Candidate: “Time O(n), space O(n). If memory were constrained and IDs were bounded, we could use a bitset, but set is simplest and clear.”
Why it works
The good answer demonstrates correct interpretation of “first,” chooses a constraint-justified approach, and validates with tests that match Amazon’s typical follow-ups. Communication is structured: clarify → propose → justify → implement → verify. The tradeoff callout (set vs bitset) shows judgment without over-engineering.
Turn this into repeatable practice with feedback
Reading frameworks helps, but Amazon performance is about execution: timing, clarity, and consistency across different problem types. On CrackTheOffer, you can run realistic new grad mock interviews with a structured rubric (problem understanding, algorithm choice, implementation, testing, and LP communication), then iterate on the exact moments where you lost time or clarity.