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Dynamic Programming / 1Dhard70 minAI/ML Engineer

Batching Ranking Budget Optimizer

AI/ML Engineer signal: dynamic programming + knapsack in a model-serving batcher context. This is a ProdMatch-owned ai ml engineer drill, framed as a April 2026 Postman Inference Platform simulation, not a copied platform question.

Company context

Postman · Inference Platform

Freshness

April 2026

Product surface

model-serving batcher

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

Question

Your Inference Platform roadmap has candidate improvements for model-serving batcher. 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.

Next Similar Problems

Vector Ranking Budget OptimizermediumGraphRAG Ranking Budget OptimizerhardRecommend Ranking Budget Optimizerhard