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This question evaluates a candidate's competency in ML system design, focusing on balancing cost-versus-latency trade-offs for inference routing, operational metrics, workload segmentation, and reliability.

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Company: Google

Role: Software Engineer

Category: ML System Design

Difficulty: medium

Interview Round: Technical Screen

You are building an AI-powered product and must choose between two inference options for each request: - **Option A:** higher cost per token, but lower latency - **Option B:** lower cost per token, but higher latency How would you decide when to use each option? Discuss the trade-offs across user experience, latency, quality, reliability, and operating cost. Also explain what metrics you would track, how you would segment different workloads, and whether you would use a dynamic routing strategy instead of a single global choice.

Overview: This question evaluates a candidate's competency in ML system design, focusing on balancing cost-versus-latency trade-offs for inference routing, operational metrics, workload segmentation, and reliability.

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Jan 10, 2026
mediumSoftware EngineerTechnical ScreenML System Design
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You are building an AI-powered product and must choose between two inference options for each request:

  • Option A: higher cost per token, but lower latency
  • Option B: lower cost per token, but higher latency

How would you decide when to use each option? Discuss the trade-offs across user experience, latency, quality, reliability, and operating cost. Also explain what metrics you would track, how you would segment different workloads, and whether you would use a dynamic routing strategy instead of a single global choice.

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