Engagement with Facebook Events

Company:Meta
Dawn AI

Welcome to Interview Master! 👋

I'm here to help you practice Engagement with Facebook Events - a real SQL interview question from Meta.

Problem Overview

As a Data Scientist on the Facebook Events Discovery team, you are tasked with analyzing user interaction with event recommendations to enhance the relevance of these suggestions. Your goal is to identify which event categories receive the most user clicks, determine if users are engaging with events in their preferred categories, and understand user engagement patterns by analyzing click data. This analysis will help optimize recommendation algorithms to increase user satisfaction and event attendance.

This problem will test your SQL skills in:

  • Complex query writing and optimization
  • Data analysis and manipulation
  • Real-world database scenarios used in Meta interviews
  • Advanced SQL concepts and best practices

What You'll Learn

By solving this Meta SQL interview question, you'll gain experience with:

  • Writing efficient SQL queries for production databases
  • Understanding complex data relationships and schema design
  • Applying SQL skills in a Meta-style technical interview setting
  • Problem-solving techniques used by data scientists and analysts at top tech companies

Getting Started

Use the SQL editor on the right to:

  1. Explore the database schema and table structures
  2. Write and test your SQL queries in a real SQLite environment
  3. Get instant feedback on your query results
  4. Learn from hints and detailed explanations

Ready to practice this Meta SQL challenge? Let's dive in! 🚀

I'm ready to work on this Meta SQL problem. Can you break down the requirements for me?

Dawn AI

Excellent! Let's break down "Engagement with Facebook Events" step by step.

Problem Analysis

Company: Meta
Problem Type: SQL Interview Question
Skill Level: Professional interview preparation

Key Requirements

As a Data Scientist on the Facebook Events Discovery team, you are tasked with analyzing user interaction with event recommendations to enhance the relevance of these suggestions. Your goal is to identify which event categories receive the most user clicks, determine if users are engaging with events in their preferred categories, and understand user engagement patterns by analyzing click data. This analysis will help optimize recommendation algorithms to increase user satisfaction and event attendance.

Approach Strategy

For this Meta SQL interview question, consider:

  1. Data Exploration: Start by examining the table schemas to understand the data relationships
  2. Query Planning: Think about which tables you'll need to JOIN and what conditions to apply
  3. SQL Optimization: Consider performance implications for large datasets (important for Meta scale)
  4. Edge Cases: Think about NULL values, duplicate data, and boundary conditions

Next Steps

  1. Click on the "Schema" tab in the SQL editor to examine the table structures
  2. Review the sample data to understand the data patterns
  3. Start with a basic SELECT statement and build complexity gradually
  4. Test your query and iterate based on the results

This type of SQL problem is commonly asked in Meta technical interviews for data analyst, data scientist, and software engineer positions. Take your time to understand the problem thoroughly before writing your solution.

Ready to start coding? 💻

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Current Question

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As a Data Scientist on the Facebook Events Discovery team, you are tasked with analyzing user interaction with event recommendations to enhance the relevance of these suggestions. Your goal is to identify which event categories receive the most user clicks, determine if users are engaging with events in their preferred categories, and understand user engagement patterns by analyzing click data. This analysis will help optimize recommendation algorithms to increase user satisfaction and event attendance.
Company: Meta
Difficulty: Medium

Tables

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fct_chat_interactions(user_id, message_id, feature_used, interaction_date)
users(id, name, department, created_at)
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Run a query to see results

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