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Dynamic Programming / 1Dhard70 minSecurity Engineer

Policy Ranking Budget Optimizer

Security Engineer signal: dynamic programming + knapsack in a authorization policy engine context. This is a ProdMatch-owned security engineer drill, framed as a May 2026 SAP Labs Identity simulation, not a copied platform question.

Company context

SAP Labs · Identity

Freshness

May 2026

Product surface

authorization policy engine

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

Question

Your Identity roadmap has candidate improvements for authorization policy engine. 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

  • attack graphs
  • policy tries
  • anomaly detection
  • 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.

Next Similar Problems

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