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Dynamic Programming / 1Dmedium45 minSoftware Engineer

Tickets Ranking Budget Optimizer

Software Engineer signal: dynamic programming + knapsack in a support ticket routing context. This is a ProdMatch-owned software engineer drill, framed as a May 2026 MoEngage Service Desk simulation, not a copied platform question.

Company context

MoEngage · Service Desk

Freshness

May 2026

Product surface

support ticket routing

ProdMatch interview simulation based on product-team patterns; not a claim of a real company question.

Question

Your Service Desk roadmap has candidate improvements for support ticket routing. Each improvement has cost, lift and risk. Maximize lift under budget while keeping total risk below a threshold.

Input

  • Items [cost, lift, risk], budget and maxRisk.

Output

  • Maximum lift achievable.

Constraints

  • 1 <= items.length <= 500
  • Budget <= 10000
  • maxRisk <= 10000

Concepts

  • invariants
  • API correctness
  • graph modelling
  • dynamic programming
  • knapsack
  • ranking trade-off

items [[2,5,1],[4,9,4]], budget=4, risk=4 -> 9

Approach

Try framing your own approach first. The 30 seconds you think before peeking is where learning happens.

Clean Solution

Reveal the approach first.

How well did you understand?

Your rating tunes when this problem shows up again.

Common Mistakes

  • Unclear state definition usually causes off-by-one bugs.
  • Do not overwrite a state before all consumers have used it.

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