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Microsoft interview questions for system design, especially those tagged with ML Systems, data engineering, web, backend, Recommendation Systems, machine learning, and System Design, typically involve open-ended prompts like "Design a recommendation system for local sports teams." No exact match for a titled problem called "Design Local Sports Team Recommendation" appears in standard public repositories such as LeetCode, Glassdoor, Levels.fyi, or interview prep sites like IGotAnOffer or Exponent, based on prior searches.
Such questions usually start with a vague prompt: Design a system to recommend local sports teams (e.g., MLB, NBA, NHL) to users based on their location, preferences, past engagement, and demographics. The goal is personalized suggestions to boost fan engagement, ticket sales, or app usage for a Microsoft product like a sports app or Azure-powered platform.
No verbatim examples exist for this title, but analogous recommendation designs provide these:
Input (API: GET /recommendations):
{ "user_id": "12345", "location": {"lat": 41.8781, "lng": -87.6298}, // Chicago "context": {"time": "2026-02-02", "device": "mobile"}, "history": ["watched Bulls game", "liked Cubs"] }
Output:
{ "recommendations": [ {"team": "Chicago Bulls", "confidence": 0.95, "reason": "Local NBA, recent views"}, {"team": "Chicago Cubs", "confidence": 0.88, "reason": "Local MLB, user history"}, {"team": "Chicago Blackhawks", "confidence": 0.75, "reason": "Local NHL suggestion"} ] }