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Amazon interview notes, page 59 of 159

FAANG / Big Tech

Most of these Amazon write-ups come from Software Engineers (1,092), then Data Scientists (182) and Product Managers (165). The most reported rounds are the technical phone screen (618), the behavioral / leadership round (309), the online assessment (228) and the system design round (159).

1581 interview notes · updated Oct 2026

Amazon·Software Engineer·Technical Phone Screen

May 2026
Interviewed for a solutions architect role at Amazon, got asked a pretty foundational data question that I thought I'd cruise through but ended up overcomplicating.
  • What is the difference between OLTP and OLAP?

“Knew this cold in theory but started rambling about row vs columnar storage and lost the thread a bit.”

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Amazon·Data Scientist·Technical Phone Screen

May 2026
Amazon Data Scientist interview with a SQL-heavy technical screen. The problem was framed around an e-commerce dashboard scenario and required both aggregation logic and window functions back to back.
  • Given a Customers table and a Sales table, write a SQL query that joins them and returns total revenue grouped by region.
  • Extend the previous query using window functions to rank customers by total spending within each region and return only the top 3 per region.

“Pretty standard join-and-aggregate question.”

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Amazon·Software Engineer·Online Assessment (OA)

May 2026
Amazon SWE online assessment with a tree traversal problem. Pretty standard stuff but the input parsing tripped me up more than the actual algorithm.
  • Given a list of employees (each with an id, importance value, and list of direct subordinate ids), compute the total importance of a target employee by summing their importance with all direct and indirect subordinates.

“Classic BFS/DFS problem once you see it.”

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Amazon·Software Engineer·Hiring Manager Screen

May 2026
Amazon EM interview with a single cross-functional problem-solving question. Felt more like a case study than a behavioral round, which I wasn't fully prepared for.
  • A program is experiencing a performance decline and multiple teams are involved, but the root cause is unknown. How do you approach diagnosing and fixing it?

“I went straight to 'let's align all the teams and set up a war room' which in hindsight was probably too operational too fast.”

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Amazon·Machine Learning Engineer·Technical Phone Screen

May 2026
Amazon ML Engineer screen, one meaty conceptual question about tree-based models. Pretty standard for this type of role but they pushed on the details more than I expected.
  • Compare decision trees and random forests. Walk through how each is built, and discuss the tradeoffs around bias, variance, overfitting, and interpretability.

“I started with the single tree stuff which felt fine, splitting on features, stopping criteria, easy.”

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Amazon·Product Manager·Onsite - Behavioral / Leadership

May 2026
Amazon PM interview, one of those behavioral rounds that sounds casual but really isn't. The question about data was the one that stuck with me.
  • Tell me about a time when data led you in the wrong direction.

“I blanked for a second because every PM story I prepped was about data saving the day.”

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Amazon·Machine Learning Engineer·Technical Phone Screen

May 2026
Amazon ML engineer interview, one technical question about neural network architectures. Pretty light on detail but the topic is a classic for this role.
  • How do LSTMs differ from traditional neural networks?

“I knew this one but still fumbled the explanation a bit.”

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Amazon·Software Engineer·Technical Phone Screen

May 2026
Applied Scientist interview at Amazon with a tricky neural network theory question that apparently almost nobody gets right. Short post but the question itself is genuinely interesting.
  • You have two two-layer neural networks: one with very few neurons per layer, one with many neurons per layer. Which one is more likely to get stuck in local minima during training?

“The interviewer mentioned fewer than 2 out of 100 candidates answer this correctly, which made me feel pretty good about getting it.”

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Amazon·Software Engineer·Technical Phone Screen

May 2026
Amazon SWE interview with two coding problems back to back, one on graphs and one on binary trees. Nothing too wild but the follow-up questions on complexity and edge cases kept things from being easy.
  • Given an undirected graph with n nodes labeled 0 through n-1 and an edge list, write a function that finds all connected components using DFS. Each component's nodes should be in ascending order, and the components themselves sorted by their smallest node label. Then discuss time and space complexity and how you'd handle very large sparse graphs.
  • Given the root of a binary tree, implement level-order traversal that groups values by level, and also write a function to check if the tree is height-balanced (left and right subtree heights differ by at most 1 at every node). Walk through complexity and the test cases you'd use.

“I got the core DFS part down pretty quickly but fumbled a bit when they asked about large sparse graphs.”

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Amazon·Software Engineer·Technical Phone Screen

May 2026
Amazon coding round, one question the whole time. Classic heap problem but the implementation details are where you can really lose points if you're not careful about the balancing logic.
  • Design a data structure that supports adding numbers from a stream one at a time and returning the median of all numbers seen so far at any point.

“I knew the two-heap approach going in, max-heap for the lower half and min-heap for the upper half.”

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