Amazon Machine Learning Engineer Interview Questions

I would structure the answer around the problem, the model, the deployment path, and the business impact. I would explain why the project mattered, what data I used, how I trained and evaluated the model, and what changed after launch. The reason I choose this structure is that Amazon interviews usually care about whether I can connect ML work to real product outcomes. I would also mention one hard trade-off or failure I encountered and how I handled it. If the interviewer wants more detail, I would talk about monitoring and iteration after launch.

I would first check whether the drop is real by validating logging and data pipelines. Then I would segment the metric by device, region, user type, and traffic source to find where the decline is concentrated. The reason I choose this approach is that metric changes are often caused by a narrow slice of traffic rather than the full population. I would also compare the release window to previous periods and look for launch changes, outages, or external events. If needed, I would continue narrowing the scope until I found the likely root cause.

I would first ask whether the missingness is random or correlated with the target or another important feature. Then I would compare options such as imputation, adding a missing-value indicator, using a domain-specific default, or dropping rows if that is acceptable. The reason I choose this approach is that the best fix depends on why the values are missing and how the feature is used. I would also think about whether missingness itself carries signal. If the interviewer wants more detail, I would explain how I would validate the choice on a held-out set.

I would join the orders and customers tables, filter orders to the last 30 days, group by customer, sum revenue, sort by the sum descending, and limit the result to 5. The reason I choose this pattern is that it is the standard way to answer a top-k revenue question cleanly. I would also think about whether refunds, canceled orders, or currency conversion should be included. If the interviewer asks, I would mention using a window function as an alternative when more detail is needed. I would keep the query simple unless they ask for edge cases.

An INNER JOIN returns only rows that match in both tables, while a LEFT JOIN returns all rows from the left table and matching rows from the right table when they exist. I would explain it with an example like customers and orders, where a LEFT JOIN can show customers even if they have no orders. The reason I choose this example is that it makes the difference easier to see in product terms. I would also mention that NULLs appear on the right side of a LEFT JOIN when there is no match. If needed, I would show how this affects reporting queries.