Amazon Interview Questions — Page 156

Amazon·Machine Learning Engineer·Onsite - Behavioral / Leadership

Jul 2026

Behavioral loop for an ML Engineer role at Amazon, pretty much all leadership principles territory. Three questions, all of them the kind where you need a real story or you're dead in the water.

Describe a time you strongly disagreed with a senior decision. How did you push back, escalate appropriately, and eventually commit once the decision was made?
Give an example of how you raised the technical bar on your team, whether through mentoring, hiring, or design quality, and what measurable impact it had.
Tell me about a failure or incident you personally owned. How did you recover quickly, and what systems or processes did you put in place to prevent it from happening again?

“This one tripped me up a little because my instinct was to tell a story where I was right and the senior person came around.”

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Amazon·Data Scientist·Technical Phone Screen

Jul 2026

Amazon data scientist interview with a case-style question about diagnosing a business decline. Pretty open-ended, which I wasn't fully prepared for.

If a key business metric suddenly drops, how would you go about diagnosing the root cause using data? Walk through the metrics you'd look at and where you'd get that data.

“I started with conversion funnel breakdowns and felt okay about that, but I fumbled when they pushed on external factors.”

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Amazon·Machine Learning Engineer·Technical Phone Screen

Jul 2026

Amazon ML Engineer interview with a tree-traversal problem dressed up as an org chart question. Clean problem statement but the tie-breaking rule is the kind of thing that'll burn you if you're not paying attention.

Given a company org chart represented as a list of employee records (each with an id, name, salary, and manager_id), find the manager whose team of all direct and indirect reports has the highest average salary. Break ties by returning the smaller id. Return -1 if no managers exist.

“The core traversal isn't the hard part.”

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Amazon·Data Scientist·Technical Phone Screen

Jul 2026

Amazon Data Scientist interview that went deep into ML fundamentals, covering ensemble methods, regularization, transformer internals, and gradient issues all in what felt like one very long breath. Technically dense and not super conversational.

Compare Random Forest and XGBoost in terms of bias-variance trade-off, training speed, and interpretability.
What is overfitting, and what are at least three techniques you would use to reduce it in a large-scale recommendation system?
What evaluation metrics would you consider for a recommendation model, and when is each one preferable?
Explain how LoRA adapts large transformer models, and contrast CNN, RNN, and Transformer architectures including why attention helps with long-range dependencies.
What causes gradient vanishing and exploding, and how do batch normalization, residual connections, and careful weight initialization each help mitigate those problems?

“I knew this one pretty well but still fumbled the interpretability angle a bit.”

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Amazon·Data Scientist·Onsite - Behavioral / Leadership

Jul 2026

Behavioral loop at Amazon for a Data Scientist role, focused almost entirely on how you handle things going sideways. Four questions, all circling the same theme: pressure, prioritization, and keeping your team from falling apart.

Tell me about a major risk or crisis that came up in the middle of a project and how you handled it quickly.
When you're working with limited resources, how do you decide what to cut or deprioritize without dropping the ball on delivery?
Walk me through how you run a post-mortem after something goes wrong. How do you make sure the same mistake doesn't happen again?
How do you keep your team motivated and productive when the project is under sustained pressure for a long stretch?

“I had a decent story ready but fumbled the 'rapidly' part.”

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Amazon·Software Engineer·Onsite - System Design / Architecture

Jul 2026

Amazon system design round for a software engineering role. The question was a massive end-to-end design covering Kafka, S3, and Presto for an ad clickstream platform. Felt like three interviews compressed into one.

Design an end-to-end advertising clickstream ingestion and analytics platform using Kafka for ingestion, S3 for storage, and Presto for interactive querying. Cover partitioning strategy, schema management, storage layout, query optimization, scale to 1M+ events per second, fault tolerance, schema evolution, PII handling, cost controls, and the trade-offs between real-time and batch processing.

“This question ate me alive a little.”

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Amazon·Data Scientist·Technical Phone Screen

Jul 2026

SQL technical screen for a Data Scientist role at Amazon. One question, pretty focused, centered on window functions and how to pull first-per-group records out of an orders table.

Given an orders table with merchant IDs, order IDs, amounts, and dates, write a SQL query that returns each merchant's first order for every calendar date they had activity.

“I knew ROW_NUMBER() was the right move but fumbled for a second on whether to partition by just merchant_id or by both merchant_id and order_date.”

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Amazon·Software Engineer·Online Assessment (OA)

Jul 2026

Took the Amazon SDE2 online assessment after six months of prep including neetcode 150 and a bunch of tagged questions, and still bombed it. The algorithm question looked like a graph problem but was actually a prefix sum variant, and I burned too much time going down the wrong path. Got rejected.

Given a coding problem with multiple failing test cases, identify and fix as many bugs as possible within the time limit.
Solve an algorithm problem that involves computing range-based aggregations over a sequence, requiring recognition of an underlying prefix sum pattern beneath a layer of abstraction.

“This was the AI coding round.”

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Amazon·Software Engineer·Online Assessment (OA)

Jul 2026

Went through Amazon's HackOn OA for an SDE Intern role. There were two sections: a DSA problem and a dev problem. Cleared all test cases on the DSA side but only got 3 out of 6 on the dev section, so now I'm wondering if anyone actually cleared both and what stack they used.

What approach and tech stack did you use to pass all test cases in the HackOn dev section?

“Got 3/6 on the dev section and I'm still not sure if it was a logic issue or something about the stack I picked.”

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Amazon·Software Engineer·Online Assessment (OA)

Aug 2026

Went through the Amazon SDE II online assessment back in August and have been stuck in limbo ever since. Coding felt fine, a couple test cases didn't pass due to what looked like a platform formatting issue rather than anything wrong with my code, and the behavioral section felt solid too. It's been 6+ weeks since the OA with no real update, just periodic replies from a substitute recruiter saying they're working on it.

How long after completing the online assessment did you hear back about next steps in the Amazon SDE II process?
Has anyone gone through a long silence of 6 to 8 weeks or more after the OA and still ended up moving forward in the process?
Is it normal for an Amazon application to stay in 'Under consideration' status for an extended period after the OA?
At what point should you realistically assume the process has stalled and there's no path forward?

“This is the thing keeping me up.”

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