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Apple·Product Manager·Onsite - Product Sense / Strategy·Intermediate

Intermediate
Jul 2026

Summary

Apple product sense interview with a single maps-related question. Pretty light on detail but the core prompt was interesting enough to think about.

Questions Asked (1)

Q1

How would you design a restaurant recommendation feature for Google Maps?

Product Sense & IdeationProduct Strategy
Author's notes

Framed it around user intent first, which felt right.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the goal and scope of the feature, then segment users and identify their key needs. Structure your answer around a user-centric design process: define, ideate, prioritize, and measure, while highlighting trade-offs and Apple's differentiators.

Pro tip: Emphasize privacy and on-device intelligence as Apple's unique advantages, and discuss how you would balance personalization with user trust.

1. Clarify Objective & Scope

Ask clarifying questions to understand the goal (e.g., increase engagement, help users discover restaurants) and constraints (e.g., data sources, privacy). Define what success looks like.

2. Understand Users & Use Cases

Identify target user segments (e.g., tourists, locals, foodies) and their needs (e.g., quick nearby options, dietary restrictions, ambiance). Consider contexts like time of day, location, and social setting.

3. Ideate & Prioritize Features

Brainstorm potential features (e.g., personalized rankings, collaborative filtering, contextual filters) and prioritize using a framework like RICE or impact/effort. Focus on high-impact, feasible ideas.

4. Design & Integrate

Sketch the user flow and integration with Google Maps (e.g., map view, search, reviews). Consider data sources (user reviews, location history) and algorithms (ML for personalization).

5. Define Metrics & Iterate

Propose success metrics (e.g., click-through rate, conversion to directions, user satisfaction) and a plan for A/B testing and iteration based on feedback.

Key Points to Mention

  • Leverage existing data: user reviews, ratings, location history, and search queries.
  • Personalization: use collaborative filtering and contextual signals (time, weather, companions).
  • Privacy: on-device processing and differential privacy to protect user data.
  • Integration with Apple ecosystem: Siri suggestions, Apple Maps, and cross-device continuity.
  • Cold start problem: how to recommend for new users or new restaurants.
  • Evaluation metrics: engagement, satisfaction, and business impact (e.g., restaurant partnerships).

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.