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coding
Easy
Verified Question
#1
1. Label Co-occurrence Finder
Category: String coding problemYou are given a list of label groups and a list of required labels. Each group is a list of strings. A group is considered valid if it contains every...Input: Array of strings
Output: Array
coding
Medium
Verified Question
#2
2. Region Grid Coloring
Category: Grid/matrix coding problemYou are given an
M x N grid of security zones. Each cell contains one of the following values: - 1 -- the zone is cleared - 0 -- the zone...Input: 2D gridOutput: Computed result
coding
Medium
Verified Question
#3
3. Parallel Task Batching
Category: Graph coding problemA pipeline must execute a set of tasks with dependency constraints. Each dependency
[A, B] means task A must complete before task B can start....Input: Graph (nodes and edges)Output: Computed result
coding
Medium
Verified Question
#4
4. Maximum Interval Overlap
Category: Interval-based coding problemYou are given a list of closed intervals on the number line, where each interval
[start, end] includes both endpoints. Find the maximum number of...Input: ListOutput: Integer
coding
Hard
Verified Question
#5
5. Interval Coverage Counter
Category: Interval-based coding problemGiven a list of closed intervals on the integer number line, build a data structure that efficiently answers point-coverage queries. A closed...Input: List
Output: Computed result
coding
Easy
Verified Question
#6
6. [CodeSignal] Movie Group Ranker
Category: Array coding problemYou are building a movie recommendation system. Given a source movie a user liked, you receive: - An array
scores where scores[i] is the...Input: ArrayOutput: Integer
coding
Easy
Verified Question
#7
7. [CodeSignal] One-Hot Encoder
Category: Matrix coding problemGiven an integer array
arr, return its one-hot encoded matrix as a 2D array. In a one-hot encoding: - Each row represents one element from arr. -...Input: Matrix (2D array)Output: Computed result
coding
Medium
Verified Question
#8
8. Event Rate Limiter
Category: String coding problemDesign a rate-limited event logger for a streaming system. Events arrive in non-decreasing timestamp order. The system must suppress an event name if...Input: String
Output: Printed output
coding
Medium
Verified Question
#9
9. Viewing History Friends
Category: Algorithm coding problemA streaming platform groups customers together based on shared viewing habits. You receive: -
customerIds - a list of distinct customer IDs -...Input: ListOutput: Array
coding
Hard
Verified Question
#10
10. Weight-Based Cache
Category: String coding problem# Weight-Based CacheInput: List
Output: Computed result
coding
Hard
graph
#1
1. [OA] Graph Traversal — Optimize genre recommendations for users
To enhance user experience and engagement, Netflix needs a system to recommend new content based on users’ viewing histories and preferences. It’s essential to identify connections between different genres and what combinations drive user engagement.
Given a directed graph represented by an adjacency list where nodes represent genres and edges indicate recommended connections, implement a function to traverse the graph and return all genres reachable from the source genre in a list.
- Implement
Input:
Output:
Explanation: Starting from genre 0, we can reach genres 1, 2, 3, and 4 through multiple recommendations.Example 2:
Input:
Output:
Explanation: Genre 1 has no further recommendations.Constraints:
-
-
Given a directed graph represented by an adjacency list where nodes represent genres and edges indicate recommended connections, implement a function to traverse the graph and return all genres reachable from the source genre in a list.
- Implement
getRecommendedGenres(genres: List[List[int]], start: int) -> List[int] which outputs all genres reachable from the start genre in any order.Example 1:Input:
genres = [[1,2],[2,3],[3,4],[5],[]], start = 0Output:
[1, 2, 3, 4]Explanation: Starting from genre 0, we can reach genres 1, 2, 3, and 4 through multiple recommendations.Example 2:
Input:
genres = [[1,2],[],[3],[],[]], start = 1Output:
[]Explanation: Genre 1 has no further recommendations.Constraints:
-
1 <= len(genres) <= 1000-
0 <= genres[i].length <= 100
coding
Hard
sliding window
#2
2. [OA] Sliding Window — Track user viewing sessions in real-time
In a dynamic streaming platform like Netflix, understanding user engagement through the length of current viewing sessions is crucial for optimizing content delivery and recommendations.
Given a list of
- You need to implement a function
Input:
Output:
Explanation: The unique sessions in the window of the last four durations are 2, 3, 4, 1, resulting in four unique sessions.Example 2:
Input:
Output:
Explanation: All durations are the same; hence, the maximum unique sessions is 1.Constraints:
-
-
-
Given a list of
int representing session durations where each duration is the time in seconds a user has spent watching content, return the maximum number of unique sessions in any continuous window of time.- You need to implement a function
maxUniqueSessions(durations: List[int], k: int) -> int that returns an int representing the maximum number of unique sessions in any window of length k.Example 1:Input:
durations = [1, 2, 3, 2, 4, 1], k = 4Output:
4Explanation: The unique sessions in the window of the last four durations are 2, 3, 4, 1, resulting in four unique sessions.Example 2:
Input:
durations = [3, 3, 3, 3, 3], k = 2Output:
1Explanation: All durations are the same; hence, the maximum unique sessions is 1.Constraints:
-
1 <= len(durations) <= 10^5-
0 <= durations[i] <= 10^5-
1 <= k <= len(durations)Related Netflix Frontend Engineer interview prep
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