Amazon Interview Questions — Page 72

Amazon·Product Manager·Onsite - Product Sense / Strategy

May 2026

PM interview at Amazon with one strategy question about the company's origin story. Short and focused, not much else to report.

Why did Amazon choose to launch with books as its first product category?

“I knew the rough answer but fumbled the structure.”

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Amazon·Software Engineer·Technical Phone Screen

May 2026

Amazon SWE coding round, one question, graph-based. Nothing too wild but it required knowing your way around topological sort.

Given a list of courses with prerequisites, return a valid order in which all courses can be completed.

“Classic topological sort.”

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Amazon·Machine Learning Engineer·Technical Phone Screen

May 2026

Amazon ML engineer interview that was basically a single coding problem stretched across four progressive stages. The Morse code theme sounds fun until you're staring at a backtracking problem at the end of it.

Implement a Morse code encoder that takes a string and converts it to Morse code with separators between characters and words.
Now decode a Morse code string back to text, assuming separators are present between characters and words.
Decode a Morse code string with no separators into a single word, given a vocabulary of valid words.
Extend the decoder to handle a Morse string with no separators that could represent multiple words from a given vocabulary, using backtracking to find valid sequences.

“Pretty straightforward as a warmup.”

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Amazon·Software Engineer·Technical Phone Screen

May 2026

ML depth round for an Applied Scientist role at Amazon, no coding involved. The session went pretty deep on the modern deep-learning stack and wrapped up with a case study on evaluating generation quality plus two leadership principle questions.

Walk me through how self-attention works in transformers, including the roles of Q, K, and V, scaled dot-product attention, and multi-head attention.
What are the differences between sinusoidal positional encodings, RoPE, and ALiBi, and when would you prefer one over another?
Explain the differences between encoder-only, decoder-only, and encoder-decoder transformer architectures, including where residual connections and layer norm are placed.
Describe attention variants like causal attention, cross-attention, sparse attention, and local windowed attention. Also explain how KV-caching works during inference.
Explain how diffusion models work, covering the forward and reverse processes, noise schedules, classifier-free guidance, and the difference between DDPM and DDIM sampling.
In the context of image generation, what are the roles of the VAE and UNet, and what makes evaluating image generation quality difficult?
What is the difference between LLM pre-training, supervised fine-tuning, and reinforcement learning from human feedback? How do scaling laws factor in, and how do you evaluate an LLM?
How would you design a pipeline to evaluate the quality of AI-generated images for brand advertisements, specifically ensuring that brand names appear correctly in the generated images?
Tell me about a time you went significantly deeper into a problem than others around you and uncovered a root cause that wasn't obvious.
Describe a time you proposed a vision or scope that was significantly larger than what your team was initially considering.

“I felt pretty solid here until they pushed on why you scale by the square root of the key dimension.”

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Amazon·Product Manager·Onsite - Product Sense / Strategy

May 2026

Amazon PM interview with a product analytics question about social sharing. One question, pretty focused, felt more like a metrics and experimentation exercise than a pure product sense one.

How would you measure whether a social sharing feature is actually working, including making sure that people who receive shared links end up installing and activating the app? How would you design an A/B test to quantify the impact?

“This tripped me up more than I expected.”

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Amazon·Data Scientist·Onsite - Cross-functional / Panel

May 2026

Amazon data scientist panel that was basically one giant scenario question about explaining a modeling decision to a non-technical sales rep. Felt more like a communication stress test than a technical interview, which I did not see coming.

Walk a non-technical Principal Sales Rep through a complex modeling decision from your resume project in under 5 minutes, covering the problem, what your approach enables, trade-offs, and risks, without using jargon.
The sales rep interrupts and asks: 'Why can't we just use a simple rule instead?' How do you respond in the moment?
When the rep probes further, how do you introduce 2 or 3 technical details like feature leakage, your validation approach, or model monitoring at a level a non-technical person can actually follow?
How do you verify that your explanation actually landed, both during and after the meeting, and what do you do if the rep later repeats something back incorrectly?
Give a concrete example from your past work using a situation-action-result structure, quantify the impact (revenue, latency, precision/recall, etc.), and reflect on what you would do differently.

“This is where I fumbled first.”

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Amazon·Software Engineer·Onsite - System Design / Architecture

May 2026

Amazon system design round focused on diagnosing performance regressions in distributed systems. Pretty technical, felt more like a debugging conversation than a traditional interview.

A distributed system is showing latency or throughput regressions. Walk me through how you'd diagnose and fix it.

“I started with metrics and logs which felt right, but I rambled a bit trying to cover everything at once.”

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Amazon·Software Engineer·Onsite - Behavioral / Leadership

May 2026

Amazon SWE behavioral round, basically one big question about deadline pressure and how you handled it. Pretty standard loop format but the question had a lot of layers to it.

Describe a time you had to ship a project under serious time pressure. What did you cut, what did you keep, and how did you keep stakeholders in the loop when things got risky?

“This one hit harder than I expected.”

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Amazon·Software Engineer·Onsite - Behavioral / Leadership

May 2026

Behavioral round at Amazon for a SWE role, basically one question but it took up the whole conversation. The ownership angle is very on-brand for them so I should've seen it coming.

Tell me about a time you took on something significant that was outside your normal area of responsibility. Why did it matter and what happened?

“I had a story ready but it felt thin under follow-up.”

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Amazon·Software Engineer·Onsite - Behavioral / Leadership

May 2026

Behavioral round at Amazon for a software engineer role. Pretty much one question stretched into a whole conversation, with a follow-up that honestly stung more than the original ask.

Tell me about a time you were criticized or called out for something at work. What happened, who gave the feedback, and did you think it was fair? How did you react in the moment and what did you do about it afterward?
Looking back at that situation, what would you do differently so it goes better next time?

“This is the kind of question where you think you have a good story until you're actually telling it and realize you're making yourself sound defensive.”

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