Amazon Interview Questions — Page 16

Amazon·Product Manager·Onsite - Product Sense / Strategy

Apr 2026

Amazon PM interview with a nonprofit scenario question about allocating a surprise charitable donation. Pretty open-ended, which I wasn't expecting for a product role.

You're the PM of a nonprofit food bank that just received a large charitable donation. How do you decide where to put the money?

“I blanked for a second because I kept trying to make it a tech product question and it really isn't.”

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Amazon·Data Scientist·Technical Phone Screen

Apr 2026

Amazon data scientist technical screen, heavy on feature engineering and preprocessing design. The whole thing felt like one long applied ML case study rather than separate questions, which I wasn't fully expecting.

You have a tabular dataset with features of very different types: count data with many zeros, heavy-tailed monetary amounts, a binary flag, and two highly correlated continuous measurements. Which features need scaling or normalization, which scaler would you pick for each, and how do you make sure you're not leaking information from the test set?
The count feature has many zeros and also some missing values. Walk through how you'd handle it: what imputation strategies would you consider, would you think about zero-inflated modeling, and how would you actually validate whichever approach you pick?
Two of your features are very strongly correlated (correlation above 0.9). Describe three different strategies for dealing with this, and explain how you'd use cross-validation to pick between them while still keeping the model interpretable.
For three different model families (a regularized linear model, a tree-based ensemble, and k-nearest neighbors), explain exactly how your preprocessing pipeline would differ and why scaling and feature correlation matter differently for each.
Design a leak-safe sklearn-style pipeline and cross-validation setup that evaluates all these preprocessing choices together. Include your choice of metrics, how you'd handle class imbalance in stratification, and how you'd statistically compare different pipeline configurations.

“I went through each feature type separately which felt right.”

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Amazon·Product Manager·Onsite - Behavioral / Leadership

Apr 2026

Amazon PM interview with a behavioral question focused on decision-making under uncertainty. Pretty standard loop format, nothing too wild, but the question had more depth to it than I expected going in.

Describe a situation where you had to decide whether to move ahead with limited information or pause to gather more data first.

“I fumbled the opening on this one.”

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Amazon·Software Engineer·Technical Phone Screen

Apr 2026

Amazon SWE coding round, one graph problem the whole time. Pretty standard stuff if you've seen it before, but the details matter more than you'd think.

Given a reference to a node in a connected undirected graph, return a deep copy of the entire graph. Each node has an integer value and a list of neighbors.

“I knew the answer involved a hash map from old nodes to new ones, but I fumbled the explanation of why you need it.”

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Amazon·Software Engineer·Online Assessment (OA)

Apr 2026

Amazon new grad OA for a SWE role, 100 minutes, two coding tasks back to back. One was a standard algorithm problem and the other was something AI-related which I hadn't really prepped for.

Solve a LeetCode-style algorithm problem within the OA time limit.
Complete an AI-related coding task, such as working with or evaluating outputs from a generative AI or language model.

“Pretty standard fare, nothing too wild.”

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Amazon·Software Engineer·Technical Phone Screen

Apr 2026

Got a coding-style question for a Research Scientist role at Amazon, just the one problem, algorithmic in nature. Pretty short session from what I can tell.

Given an array of integers and a value maxdiff, partition the array into the fewest possible groups such that no two elements within the same group differ by more than maxdiff. How many groups do you need?

“Sorting is the key move here and I think I would've fumbled around with it if I hadn't seen similar interval/grouping problems before.”

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Amazon·Software Engineer·Online Assessment (OA)

Apr 2026

Amazon SWE online assessment with two algorithmic problems back to back. Pretty standard OA format, nothing too surprising, but the second problem had more moving parts than I expected.

Given a string, count the number of ways to split it into exactly two non-empty contiguous parts (a prefix and a suffix) such that the count of distinct characters shared by both parts exceeds k.
Given an array of item costs, find the maximum number of packages you can form where each package has at most two items, all packages share the same total cost, and no item is used more than once.

“Spent way too long overthinking the data structure here.”

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Amazon·Software Engineer·Onsite - Behavioral / Leadership

Apr 2026

Amazon SWE interview with a scenario-based question about handling a code reviewer who wants to go a different direction. Pretty behavioral but dressed up as a situational judgment thing.

A teammate named Ryan is reviewing your code and pushes for a more complex, long-term solution. You have three options: meet with Ryan one-on-one to talk through his concerns, set up a team meeting so the group can weigh in, or just let Ryan make the changes himself. How do you rank these from most to least effective?

“I went back and forth on this.”

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Amazon·Software Engineer·Technical Phone Screen

Apr 2026

Amazon SWE coding round, one algorithmic question, nothing fancy. The kind of problem that looks straightforward until you're actually writing it out under pressure.

Given an unsorted integer array, find the smallest missing positive integer.

“I went straight for sorting, which works but isn't the answer they're fishing for.”

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Amazon·Data Scientist·Hiring Manager Screen

Apr 2026

Behavioral interview for a Data Scientist role at Amazon with a hiring manager known to be pretty selective. The whole thing was BQ-focused, no coding, just story-based questions about past work and your motivations for switching into data.

Tell me about a project where you used data to solve a business problem. What was the problem, what data did you use, what analysis did you run, and what decision changed because of it?
Describe a project you worked on that had real measurable impact. How do you define impact, and what was your personal contribution versus the team's?
Have you dealt with data quality problems? Walk me through the issue, how you found it, how you measured it, how you fixed it, and what you put in place to stop it from happening again.
Why are you moving from finance into data? What's driving that, what skills carry over, and what concrete proof do you have that you can actually do the job?

“This is the one I spent the most time prepping and still felt like I rambled.”

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