A candidate who just wrapped an Amazon New Grad SDE loop came to us for a full debrief. The structure was very typical: one pure coding round, one pure behavioral round, and one mixed coding + BQ round. None of the problems were especially hard, but across the loop one thing was clear — the depth of evaluation keeps rising round after round. They do not just check whether you can solve it; they look at whether your communication is clear, your thinking is systematic, and whether you have truly internalized the Leadership Principles (LPs). Below is a round-by-round breakdown, with complete Python code for all three problems and an LP-to-STAR mapping table.
Interview Overview
| Item | Details |
|---|---|
| Company | Amazon |
| Role | SDE (New Grad) |
| Rounds | 3: 1 pure Coding + 1 pure BQ + 1 mixed |
| Length per round | 40–45 minutes |
| Difficulty | LeetCode Easy–Medium, standard high-frequency |
| Focus | Clarity of expression + systematic thinking + LP internalization + STAR structure |
Round 1: Pure Coding (2 problems / 45 min)
Very Amazon-efficient — a couple of pleasantries and then straight into livecoding, two problems packed into 45 minutes.
Problem 1: Valid Palindrome II (delete at most one char)
Given a string, decide whether it can become a palindrome after deleting at most one character.
Approach
Two pointers converging from both ends. If the characters match, keep going. The moment they mismatch, there are two "rescue" options — either delete the character at the left pointer, or delete the one at the right pointer. If either resulting segment is a palindrome, the whole thing works. Use a helper to check whether a given range is a palindrome.
Corner cases the interviewer asked about: empty string (counts as a palindrome) and single character (palindrome).
Python Solution
def valid_palindrome_ii(s: str) -> bool:
"""Return True if s can become a palindrome by deleting at most one char."""
def is_palindrome(left: int, right: int) -> bool:
while left < right:
if s[left] != s[right]:
return False
left += 1
right -= 1
return True
i, j = 0, len(s) - 1
while i < j:
if s[i] != s[j]:
# First mismatch: try deleting the left char OR the right char
return is_palindrome(i + 1, j) or is_palindrome(i, j - 1)
i += 1
j -= 1
return True # empty / single char / already a palindrome
Time complexity: O(n), at most one linear helper scan is triggered. Space complexity: O(1).
Problem 2: Insert Interval and Merge
Given a set of intervals that are sorted by start and non-overlapping, plus one new interval, insert it and merge any overlaps, returning the new interval list.
Approach
A single linear scan in three phases:
- Append all intervals that are entirely to the left of the new one (
end < new_start) as-is. - Merge all intervals that overlap with the new interval: keep updating the start with
minand the end withmax. - Append the remaining intervals that are entirely to the right.
The interviewer asked "why not use binary search to locate the insertion point" — the answer: this problem is about merging the overlapping segment away, which still requires a linear sweep over the overlaps. A one-pass linear scan is more intuitive and more robust; binary search only saves the locating step, doesn't change the overall complexity, and is easier to get wrong at the boundaries.
Python Solution
def insert_interval(intervals, new_interval):
"""Insert new_interval into sorted, non-overlapping intervals and merge."""
result = []
new_start, new_end = new_interval
i, n = 0, len(intervals)
# 1) Intervals entirely left of the new one: append as-is
while i < n and intervals[i][1] < new_start:
result.append(intervals[i])
i += 1
# 2) Overlapping intervals: merge one by one
while i < n and intervals[i][0] <= new_end:
new_start = min(new_start, intervals[i][0])
new_end = max(new_end, intervals[i][1])
i += 1
result.append([new_start, new_end])
# 3) Remaining intervals entirely to the right: append
while i < n:
result.append(intervals[i])
i += 1
return result
Time complexity: O(n), a single pass. Space complexity: O(n), for the result list.
Round 2: Pure Behavioral (3 questions / 40 min)
This round was led by an EM (Engineering Manager) and was entirely LP-driven. Amazon BQ is not a formality — the interviewer follows your answer down layer by layer, checking whether what you describe actually happened and how you were reasoning at the time.
Question 1: Tell me about a time you disagreed with a teammate or manager
LP: Have Backbone; Disagree and Commit.
The candidate told the "insisted on delaying launch because a dependency wasn't ready" story: he used data to explain the risk of shipping on the original schedule and fully voiced his objection; but once the decision was overridden, he chose to commit and threw himself into executing it. What the interviewer is really watching for is whether you genuinely listened to the other side and could collaborate afterward, rather than clinging to your own view.
Question 2: Ownership
Tell me about a time you stepped up on something outside your scope. The candidate described an on-call incident: he dug through logs, fixed the corrupted data, and added monitoring alerts on top. The interviewer probed: "Did you ask for permission, or just take initiative?" His answer: "I communicated the issue clearly to the relevant people first, made sure everyone was informed, and then took ownership." — that shows ownership without turning into acting unilaterally.
Question 3: Failure
Tell me about a failure. The candidate described overestimating the load, which crashed the service, and focused on how he ran the retrospective afterward and produced concrete action items. What Amazon wants to see is how you face failure, not how you repackage a failure as "not really a failure." This matters — trying to downplay the failure tends to cost you points.
Round 3: Mixed (2 BQ + 1 Coding)
The interviewer was a young, fast-talking engineer. Two BQs in the first half, one string problem in the second.
BQ 1: Earn Trust
The candidate described introducing a type system into a frontend project. At first it was seen as overengineering; he proved the value through follow-up data on maintainability and shipping efficiency. The interviewer pushed: "What if they still disagreed?" — a mature answer: "I'd ask them what success looks like in their eyes, then align on that outcome rather than getting stuck on the specific implementation."
BQ 2: Invent and Simplify
Tell me about a time you simplified a process in an inventive way. The candidate described building an automation tool that linked API docs to code generation. The interviewer asked: "How did you measure its effectiveness?" — the answer: he compared the time the manual process took against the time the tool saved, letting quantitative metrics do the talking.
Coding: Decode String
The coding problem this round was string expansion. Here we use the canonical LeetCode 394 form: decode 3[a]2[bc] into aaabcbc, and 3[a2[c]] into accaccacc.
Approach
Use a stack to handle the nested structure. Iterate over the string:
- On a digit: accumulate a possibly multi-digit number.
- On
[: push the "string built so far" and the "current repeat count" onto two stacks, then reset and enter a new level. - On
]: pop the repeat count and the previous level's string, repeat the current level's string that many times, and append it after the previous level. - On a letter: append directly to the current level's string.
Two stacks: one for string fragments, one for repeat multipliers.
The interviewer really liked proactive test cases, and asked about the time/space complexity trade-off.
Python Solution
def decode_string(s: str) -> str:
"""Decode an encoded string of the form 3[a]2[bc] (LeetCode 394)."""
num_stack = [] # repeat counts
str_stack = [] # string built before entering the current level
current = "" # string being built at the current level
num = 0 # accumulated number (supports multi-digit)
for ch in s:
if ch.isdigit():
num = num * 10 + int(ch) # handle multi-digit numbers like 12[a]
elif ch == '[':
num_stack.append(num) # save the repeat count
str_stack.append(current) # save the previous level's content
num = 0
current = ""
elif ch == ']':
repeat = num_stack.pop()
prev = str_stack.pop()
current = prev + current * repeat # expand this level, join back up
else:
current += ch # ordinary letter
return current
Time complexity: O(N), where N is the total length of the decoded output (each output character is built once). Space complexity: O(N), stack depth scales with nesting depth and output length.
LP → STAR Mapping Table
An Amazon BQ is essentially "use one real story to prove you embody a particular LP." Prepare at least 1–2 stories per LP and organize each strictly around STAR: Situation → Task → Action → Result (ideally with quantitative metrics).
| Interview question | Mapped LP | STAR key points |
|---|---|---|
| Disagreed with teammate/manager | Have Backbone; Disagree and Commit | Situation: dependency-not-ready risk; Action: voice objection with data, fully commit after being overridden; Result: shipped collaboratively on schedule |
| Stepped up outside scope | Ownership | Situation: on-call incident; Action: read logs, fix corrupt data, add monitoring, inform first then take over; Result: recovered and prevented recurrence |
| A failure | Learn and Be Curious / Deliver Results | Situation: overestimated load caused crash; Action: run retrospective, list action items; Result: faced it head-on and captured improvements |
| Persuade others to adopt your approach | Earn Trust | Situation: type system seen as overengineering; Action: prove value with maintainability + shipping-efficiency data; Result: align on outcome, not implementation |
| Simplify a process inventively | Invent and Simplify | Situation: docs and code drift apart; Action: build automation tool linking generation; Result: compare manual vs tool time, quantify the gain |
Prep Strategy
Amazon's question types haven't shifted much over the years, but the depth of detail, completeness of reasoning, and degree of LP internalization keep rising. Three suggestions:
- BQ is not storytelling, it's structured thinking. Many people think the behavioral round is about telling a compelling story, but Amazon wants STAR + quantitative metrics — using structure to explain your choices, behavior, and judgment. Prepare 1–2 stories per LP, write bullets first, rehearse out loud, then simulate the follow-ups ("Why did you judge it that way?" "How did you measure effectiveness?").
- Coding is standardized high-frequency problems. Patterns like two pointers, intervals, and stacks come up repeatedly. Solving it is only the baseline; the real bonus points are: explaining the logic clearly, proactively naming improvements, stating time/space complexity, and writing test cases that cover edges.
- Cover edge cases proactively. Empty string, single character, multi-digit numbers, empty intervals — the interviewer will almost certainly ask. Rather than waiting for the question, raise them yourself.
FAQ
Q1: How many rounds is an Amazon New Grad SDE loop, and how hard is it?
Typically a 3–4 round loop (this one was 3), commonly structured as pure coding, pure BQ, and a mixed coding + BQ round. Problems are mostly LeetCode Easy–Medium high-frequency standards, so raw difficulty is not high, but the bar on clarity of expression and LP understanding rises round by round.
Q2: What is Amazon's BQ actually testing?
Whether you embody the corresponding Leadership Principles, and whether you can tell a real experience in a structured way. The interviewer follows your answer down into the details to judge whether the story is genuine and what your reasoning was at the time. Organizing with STAR and bringing quantitative results matters far more than telling a flashy story.
Q3: For the "failure" question, should I pick an example that "wasn't really that bad"?
Not recommended. Amazon explicitly wants to see how you face a real failure — how you run the retrospective and capture improvements. Packaging a failure as "not really a failure" instead reveals that you're unwilling to confront problems, and usually costs points. Pick something you genuinely messed up but improved on afterward.
Q4: I solved the coding problem — why could I still be rejected?
Because "solving it" is only the baseline. The interviewer is simultaneously evaluating: whether your reasoning is clearly expressed, whether you proactively analyze complexity, whether you write test cases covering edges, and whether you can give a convincing trade-off when facing a follow-up (like "why not binary search"). Communication and engineering judgment matter as much as correctness.
Q5: For the insert-interval problem, would binary search be better?
You can locate the insertion point with binary search, but the core of this problem is merging the overlapping intervals away, which still requires a linear sweep over the overlaps. A single linear pass is more intuitive and more robust at the boundaries; binary search only saves the locating step, doesn't change the overall complexity, and is easier to get wrong. Being able to articulate this trade-off in the interview is itself a plus.
Preparing for an Amazon SDE interview? If you want to be more at ease with the expressive details of coding and the STAR structure of BQ, we can help with targeted mock interviews and LP story polishing.
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