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Evaluate Optimal Jogging Routes Feature with A/B Testing

Last updated: Mar 29, 2026

Quick Overview

This question evaluates proficiency in experimental design, product analytics, metric definition, randomization strategy, guardrail selection, power/MDE calculations, and event instrumentation for a mapping/navigation feature recommending jogging routes.

  • medium
  • Google
  • Analytics & Experimentation
  • Data Scientist

Evaluate Optimal Jogging Routes Feature with A/B Testing

Company: Google

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: medium

Interview Round: Technical Screen

##### Scenario Google Maps plans a feature that recommends optimal jogging routes. ##### Question How would you evaluate whether this idea is valuable for users and the business? Design an experiment for launch: success metrics, triggering logic, unit of randomization, guardrails, and minimum detectable effect (MDE). ##### Hints Define primary KPI (e.g., completed jogs), secondary metrics, power assumptions, and how to log route-recommendation events.

Quick Answer: This question evaluates proficiency in experimental design, product analytics, metric definition, randomization strategy, guardrail selection, power/MDE calculations, and event instrumentation for a mapping/navigation feature recommending jogging routes.

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Google
Jul 12, 2025, 6:59 PM
Data Scientist
Technical Screen
Analytics & Experimentation
11
0

Evaluate and Experiment: Jogging Route Recommendations in Google Maps

Context

Google Maps is considering a feature that recommends optimal jogging routes (e.g., safe, scenic, appropriate distance/elevation) when a user indicates intent to go for a run.

Task

Design how you would:

  1. Assess whether the idea is valuable for users and for the business.
  2. Launch an experiment to validate impact, including:
    • Primary and secondary success metrics
    • Triggering/eligibility logic
    • Unit of randomization
    • Guardrail metrics
    • Minimum Detectable Effect (MDE) and power assumptions
    • How to log/track recommendation events

Hints

  • Define a primary KPI (e.g., completed jogs) and secondary metrics (adoption, satisfaction, safety proxies, engagement).
  • Specify how a “trigger” is defined and when a user is exposed.
  • Choose a randomization unit that minimizes contamination.
  • Provide MDE math with concrete numeric assumptions.
  • Outline the event schema for impressions, accepts, starts, and completes.

Solution

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