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Dynamic Programming / 1Dhard70 minSecurity Engineer

Anomaly Ranking Budget Optimizer

Security Engineer signal: dynamic programming + knapsack in a login anomaly detector context. This is a ProdMatch-owned security engineer drill, framed as a April 2026 Groww Account Trust simulation, not a copied platform question.

Company context

Groww · Account Trust

Freshness

April 2026

Product surface

login anomaly detector

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

Question

Your Account Trust roadmap has candidate improvements for login anomaly detector. 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.

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