Dynamic Programming / 1Dhard70 minAI/ML Engineer

Beam Ranking Budget Optimizer

AI/ML Engineer signal: dynamic programming + knapsack in a beam-search decoder context. This is a ProdMatch-owned ai ml engineer drill, framed as a May 2026 ShareChat GenAI Runtime simulation, not a copied platform question.

Company context

ShareChat · GenAI Runtime

Freshness

May 2026

Product surface

beam-search decoder

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

Question

Your GenAI Runtime roadmap has candidate improvements for beam-search decoder. 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

vector search
RAG retrieval
recommendation graphs
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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