How should I practice Dynamic Broadcast Times?

Start by reading the full statement, solve the visible examples, then compare the pattern against related Netflix phone screen problems in FastPrep.

Is Dynamic Broadcast Times associated with Netflix?

FastPrep tags this as a Netflix phone screen coding practice problem when the problem catalog has that company and stage context.

Problem · Graph

Dynamic Broadcast Times

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● Hard

NetflixFULLTIMEPHONE SCREEN

Problem statement

A network contains n cities numbered from 0 to n - 1. Each directed connection has a positive ping time. The row edges[i] = [from, to, weight] defines edge i; edge indices remain fixed throughout the operation sequence.

Process the operations in order:

[0, edgeIndex, newWeight] replaces the weight of the existing edge at edgeIndex with newWeight.
[1, source] asks for the fastest broadcast time from source to every city using the current weights.

For every query, append an array of length n in city-index order. The distance to source is 0, and the distance to an unreachable city is -1. Return the query-result arrays in operation order.

Function

processBroadcastOperations(n: int, edges: long[][], operations: long[][]) → long[][]

Examples

Example 1

n = 4edges = [[0,1,5],[0,2,20],[1,2,4],[2,3,3]]operations = [[1,0],[0,1,6],[1,0],[0,0,15],[1,0]]return = [[0,5,9,12],[0,5,6,9],[0,15,6,9]]

The first query reaches city 2 through city 1. Lowering edge 1 makes the direct route faster. Raising edge 0 later changes city 1 without affecting the direct route to city 2.

Example 2

n = 3edges = [[0,1,2]]operations = [[1,0],[1,2],[0,0,7],[1,0]]return = [[0,2,-1],[-1,-1,0],[0,7,-1]]

City 2 cannot reach either other city. Updating the only edge changes the distance from city 0 to city 1, while city 2 remains unreachable.

Example 3

n = 5edges = [[0,1,10],[0,2,2],[2,1,3],[1,3,2],[2,3,20],[3,4,1]]operations = [[1,0],[0,4,1],[1,0],[1,2]]return = [[0,5,2,7,8],[0,5,2,3,4],[-1,3,0,1,2]]

After edge 4 decreases to weight 1, the route from city 0 through city 2 reaches cities 3 and 4 sooner. The final query uses city 2 as its source.

Constraints

1 <= n <= 500.
0 <= edges.length <= 5000.
Every edge is [from, to, weight], where both endpoints are valid city indices and 1 <= weight <= 10^9.
1 <= operations.length <= 5000, and at least one operation is a query.
Every update is [0, edgeIndex, newWeight], where edgeIndex names an existing edge and 1 <= newWeight <= 10^9.
Every query is [1, source], where 0 <= source < n.
Every shortest-path distance fits a signed 64-bit integer.