Netflix Data Scientist Coding Questions

Netflix needs an efficient SQL query to derive average movie ratings from a large dataset spanning various genres for personalized recommendations. This should support querying by specific genres and timeframes.Problem statement: Given a movies table with id, title, rating, and genre, write a SQL query to return the average rating for each genre in a specified from_date and to_date range.Example 1:
Input: from_date = '2022-01-01', to_date = '2022-12-31'
Output: genre | average_rating
Action | 8.5
Comedy | 7.7Example 2:
Input: from_date = '2021-05-01', to_date = '2021-10-01'
Output: genre | average_rating
Drama | 8.0
Documentary | 7.0Constraints:
- Movie records are relevant for the last 5 years.
- Each movie belongs to at least one genre.

Netflix needs an efficient algorithm to optimize recommendations by analyzing user watch times across multiple shows. The goal is to find the longest sequence of shows that users have watched in a single binge-watching session with a view time >= k.Problem statement: Given an integer array viewTimes, representing the view time of each show watched in order, and an integer k, return the length of the longest subarray where the sum of the view times is at least k. Utilize a sliding window approach to achieve optimal performance.- Method Signature: def longest_binge_watch(viewTimes: List[int], k: int) -> int: — returns the length of the longest subarray.Example 1:
Input: viewTimes = [1, 2, 3, 4, 5], k = 9
Output: 3
Explanation: The longest sequence with a total view time >= 9 is [3, 4, 5].Example 2:
Input: viewTimes = [2, 1, 5, 2, 3, 2], k = 7
Output: 5
Explanation: The longest sequence with a total view time >= 7 is [1, 5, 2, 3].Constraints:
- 1 <= viewTimes.length <= 10^5
- 1 <= viewTimes[i] <= 10^4
- 1 <= k <= 10^6