Amazon Interview Questions — Page 145

Amazon·Software Engineer·Onsite - Behavioral / Leadership

Jul 2026

Principal-level behavioral loop at Amazon, focused entirely on leadership depth. Every question had heavy follow-ups pushing for specific metrics, mechanisms, and conflict details. Not a loop where you can coast on vague stories.

Tell me about a project you led that failed. What happened and what did you take away from it?
Describe a time you pushed a decision through that others disagreed with. How did you handle the resistance?
Tell me about a time you changed course on a plan you had already committed to after getting new information.
Walk me through a situation where you refused to build something or said no to a request from leadership.
Tell me about a time you disagreed with your skip-level manager or a VP. What did you do?
Describe a situation where you had to fix a significant problem that someone else created. How did you approach it?

“The follow-ups are where this one gets brutal.”

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Amazon·Data Scientist·Technical Phone Screen

Jul 2026

Amazon Data Scientist coding round, three algorithm problems back to back with a requirement to justify complexity and handle edge cases out loud. Pretty intense for what I expected to be a standard screen.

Given a price array, find the maximum profit from a single buy/sell transaction and return the profit along with the buy and sell day indices. Return (0, -1, -1) if no profitable trade exists. Must run in O(n) time and O(1) space, and handle edge cases like strictly decreasing arrays or ties in profit.
Given an unweighted directed graph with up to 100,000 nodes, a source, a target, and a set of forbidden nodes, find the lexicographically smallest shortest path from source to target that avoids forbidden nodes using BFS. Explain how to achieve O(N+M) time without sorting neighbors on every BFS expansion.
Merge a list of half-open intervals [l, r) where intervals that touch or overlap should be combined into one. Handle 32-bit integer boundaries, negative values, and potential overflow. Prove that the total covered measure equals the sum of the merged interval lengths, and provide edge case tests.

“I got the basic O(n) scan pretty fast, track running min and check profit at each step.”

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Amazon·Data Scientist·Technical Phone Screen

Jul 2026

Amazon data scientist interview that went deep into econometrics territory. Five questions, all technical, covering causal inference methods I thought I knew until I had to explain them out loud under pressure.

Derive the standard 2x2 difference-in-differences estimand from the parallel trends assumption, then show it maps to a two-way fixed effects regression with a treatment-post interaction term under homogeneous treatment effects.
Why does two-way fixed effects break down with staggered treatment adoption and heterogeneous effects? Walk through the Sun-Abraham and Callaway-Sant'Anna approaches and explain how you'd construct an event study with proper cohort weighting.
How would you decide what level to cluster standard errors at when you have both household-level interference and market-level shocks? When would you use wild cluster bootstrap instead, and what goes wrong if you have too few clusters?
Design a plausibly exogenous instrument for reminder email exposure, write out the two-stage least squares setup including first stage and structural equation, and state the GMM moment conditions.
What tests would you run to check instrument strength and validity? How do you interpret a situation where the first-stage F-stat is around 8 and the Hansen J test is insignificant, especially under heteroskedasticity and clustering?

“I can do this derivation on paper but explaining it verbally while keeping notation clean is a different beast.”

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Amazon·Machine Learning Engineer·Technical Phone Screen

Jul 2026

Amazon ML Engineer interview, all technical fundamentals, no system design or behavioral that I can recall. It felt like a grad school exam more than a job interview, which I was not fully prepared for.

What is the bias-variance trade-off, and how do regularization techniques address it?
Derive the gradient of the loss function for logistic regression when L2 regularization is applied.
Compare ROC-AUC and PR-AUC. When should you prefer one over the other?
How do you detect and prevent data leakage when doing cross-validation?
What approaches exist for handling class imbalance, and how do they affect model calibration?
In what situations would you choose a tree-based model over a linear model?
How do you evaluate whether a model is well-calibrated, and what can you do to improve calibration?

“Bread and butter question but I still fumbled the explanation a bit.”

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Amazon·Software Engineer·Technical Phone Screen

Jul 2026

Amazon frontend interview, one coding question focused on building a UI component from scratch. Pretty standard for the role but it took more thought than I expected.

Build an image carousel that displays a sequence of images, using either React or vanilla JS.

“I went with React because that's what I'm most comfortable with, but looking back I wonder if vanilla JS would've impressed them more since it shows you understand the fundamentals without leaning on a framework.”

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Amazon·Data Scientist·Technical Phone Screen

Jul 2026

Amazon data scientist interview, looked like a technical screen but the prompt was basically a 30-minute timed case study on exploratory data analysis and dashboard design. Dense question, lots of moving parts, felt like they were testing whether you'd panic or actually have a system.

You're given an undocumented CSV mixing user events and purchases. In 30 minutes, walk through how you'd understand the dataset and build executive-ready visualizations. Specifically: what are your first 10 data checks, what charts would you propose, how do you choose aggregation granularity for a launch week, how would clock-skew issues show up in your visuals, and how would you describe a one-slide dashboard to a stakeholder?

“This was a lot to hold in your head at once.”

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Amazon·Software Engineer·Technical Phone Screen

Jul 2026

Amazon SWE interview with a scheduling/greedy algorithm problem. The question had a clean problem statement but the O(n log n) correctness argument took more thought than I expected.

Given a set of tasks each with a positive duration and a deadline, you can run only one task at a time in any order. What is the maximum number of tasks you can complete before their respective deadlines, and can you describe an O(n log n) algorithm with a correctness argument?

“I knew this was a greedy problem pretty fast but fumbled explaining WHY the greedy choice is safe.”

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Amazon·Software Engineer·Online Assessment (OA)

Jul 2026

Amazon OA for a SWE role. One algorithmic problem involving subarray beauty calculations, which sounds deceptively manageable until you're actually staring at it.

Given an integer array and a size k, compute the total 'beautiness' of the array, where the beauty of a subarray is the count of indices that are strictly greater than all elements to their right within that subarray. Sum this across all subarrays of size k.

“The problem reads cleanly but I wasted time on the naive O(n^2 * k) approach before realizing it was going to time out.”

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Amazon·Product Manager·Onsite - Product Sense / Strategy

Jul 2026

Amazon PM interview with a product strategy question focused on UPI, India's instant payments system. Pretty niche topic if you're not familiar with the fintech space there.

How would you grow revenue for a UPI-based payments product in India?

“I fumbled the setup a bit because UPI is famously zero-fee for consumers, so the obvious monetization levers aren't really there.”

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Amazon·Data Scientist·Technical Phone Screen

Jul 2026

SQL and data engineering heavy screen for a Data Scientist role at Amazon. Five questions, all technical, covering joins, set operations, window functions, views, and query optimization at scale. No behavioral stuff at all, which surprised me.

Given a Customers table and an Orders table (with one order referencing a non-existent customer), what are the exact row counts for INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN, and CROSS JOIN? Explain why each count is what it is.
Using tables A (with values 1, 2, 2, 3) and B (with values 2, 3, 4), what are the row count and contents of UNION vs UNION ALL?
For the Scores table filtered to 2025-08-31, compute RANK() and DENSE_RANK() over score DESC and list the expected output rows with user_id, score, rank, and dense_rank.
What is the difference between a VIEW and a MATERIALIZED VIEW? Give a pro and con of each for analytic workloads, and propose a useful view definition for the given schema.
Define PRIMARY KEY, FOREIGN KEY, and PARTITION KEY. If Orders grows to 1 billion rows with an order_date column added, what partitioning strategy and query tuning steps would you use? Also rewrite this naive query for performance: SELECT * FROM Orders o JOIN Customers c ON o.customer_id = c.cust_id WHERE o.amount > 40 ORDER BY c.name.

“This tripped me up more than it should have.”

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