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Dynamic Programming / 1Dhard70 minSoftware Engineer

Checkout Ranking Budget Optimizer

Software Engineer signal: dynamic programming + knapsack in a checkout reliability context. This is a ProdMatch-owned software engineer drill, framed as a March 2026 Chargebee Payments Core simulation, not a copied platform question.

Company context

Chargebee · Payments Core

Freshness

March 2026

Product surface

checkout reliability

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

Question

Your Payments Core roadmap has candidate improvements for checkout reliability. 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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