← Amazon Interview Insights

Amazon·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Jul 2026

Summary

Amazon BI Engineer interview with a SQL problem centered on streaming data. Pretty straightforward setup but the aggregation logic requires you to think carefully about what 'first watched' actually means per user before you can count anything useful.

Questions Asked (1)

Q1

Given a table of customer movie views with a date column, write a SQL or Python solution to find which movie title appears most often as the first film a user ever watched.

Algorithms & Data StructuresProduct Analytics & MetricsData Modeling
Author's notes

My first instinct was to just GROUP BY title and COUNT, which is completely wrong.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

First, identify the earliest view date for each user to determine their first film. Then, count how many times each movie title appears as a first film and select the one with the highest count. Use a subquery or window function to find the first film per user, then aggregate.

Pro tip: Clarify how to handle ties (e.g., if multiple movies have the same highest count) and mention that you'd validate the result with a quick sanity check, such as ensuring the total counts match the number of users.

1. Understand the data and requirements

Identify the table schema (user_id, movie_title, view_date) and confirm that 'first film' means the movie with the earliest view_date per user. Ask about tie-breaking if a user watched multiple movies on the same earliest date.

2. Find each user's first film

Use a subquery with MIN(view_date) per user or a window function like ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY view_date) to select the first movie for each user.

3. Count occurrences of each movie as first film

Aggregate the results from step 2 by movie_title, counting how many users had that movie as their first film.

4. Identify the most frequent first film

Order the counts in descending order and select the top movie. If ties are possible, decide whether to return all tied movies or just one (e.g., using LIMIT 1).

5. Validate and discuss edge cases

Check that the sum of counts equals the number of distinct users. Discuss handling of ties, nulls, and performance considerations for large datasets.

Key Points to Mention

  • Use of window functions (e.g., ROW_NUMBER, RANK) or subqueries to find the first film per user.
  • Handling ties: if multiple movies share the earliest date for a user, decide whether to pick one arbitrarily or include all.
  • Aggregation with GROUP BY and ORDER BY to count and rank movie titles.
  • Performance considerations: indexing on user_id and view_date, and avoiding full table scans.
  • Validation: cross-checking the total count against the number of distinct users.
  • Edge cases: users with no views, null dates, or multiple first films on the same date.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.