SQL Interview Questions

Q: What are the most common SQL interview questions?

The most common SQL interview questions test JOINs (INNER, LEFT, RIGHT), window functions (ROW_NUMBER, RANK, LAG/LEAD), aggregations (GROUP BY, HAVING), CTEs and subqueries, and CASE WHEN statements. Companies like Meta, Google, and Amazon frequently ask about deduplication, top-N per group, and funnel analysis patterns.

Q: How should I prepare for SQL interviews at FAANG companies?

Focus on mastering window functions, complex JOINs, and CTEs. Practice 50-100 Medium difficulty problems, timing yourself to solve each in 15-20 minutes. Learn company-specific patterns: Meta emphasizes metrics and user behavior analysis, Amazon tests data aggregation and reporting, Google focuses on analytical thinking with SQL. Practice explaining your thought process out loud.

Q: What difficulty level should I practice for SQL interviews?

Start with Easy problems to build confidence and master fundamentals. Focus 70% of your time on Medium difficulty, which represents most real interview questions. Practice Hard problems if interviewing for senior roles (L5+) or companies known for difficult technical screens like Meta or Google. Aim to solve Medium problems in 15-20 minutes.

Q: Are SQL interviews the same for Data Scientists and Data Analysts?

Both roles are tested on SQL fundamentals, but with different emphasis. Data Scientists face more complex analytical queries involving statistical calculations, user cohorts, and A/B testing analysis. Data Analysts are tested more on reporting queries, business metrics, and dashboard-ready outputs. Data Scientists typically get 1-2 harder SQL problems, while Analysts may get 2-3 medium problems in the same time.

Q: Can I use online resources during SQL interviews?

Most companies (Meta, Google, Amazon, Netflix) do NOT allow internet access during SQL interviews. You're expected to write queries in shared editors like CoderPad without running them. Exceptions include Robinhood and DoorDash which allow searching, but relying on search wastes valuable time. Practice writing syntactically correct SQL without auto-complete or execution.

SQL Interview Questions

All SQL Questions

Retrieve First Active and Last Inactive Dates per User

Calculate Response Rate and Compare User Survey Ratings

Select Top Customers Using Transaction Data Filters

Analyze Conversation Engagement and Reaction Usage Effectively

Analyze Group Call Adoption Using SQL Queries

Analyze Spending Patterns and Restaurant Performance Using SQL/Python

Calculate Average Session Length and Compare App Performance

Calculate Total Revenue in USD Using SQL Query

Identify Top Three Active Users by Event Date

Monitor Friend-Request System for Quality and Abuse

Identify Top-3 Users by Recent Total Spend

Compute User Group Stories and Aggregate Story Engagement

Calculate Engagement Metrics for Info-Stream Content Analysis

Generate Weekly Revenue and Engagement Summary with Pandas

Calculate User Registration Date and 7-Day Retention Rate

Improve YoY Revenue Analysis with Complementary Metrics

Convert Dates to Calendar Quarter Labels in SQL/Python

Design Schema for Accurate Subscription State Tracking

Understand SQL Aggregations and Joins: Key Differences Explained

Identify Top Spenders and Segment Customers Using Python

Clean and Aggregate Transactions for Finance Dashboard

Analyze User Engagement with SQL Queries

Calculate Video Call Usage Metrics by Country and Date

Identify Users Interested in Group Video Calls

Determine Tutorial Completion Rate by Variant in 7 Days

Identify Unique Callers and French Customer Call Percentage

Design Incremental Load Process for Large Relational Table

Understand SQL: DELETE vs TRUNCATE, VIEW vs TABLE, CROSS JOIN

Calculate User Deviation from Team Average Messages

Count, Return, Find, and Select in SQL Queries

Calculate Weekly, Monthly Watch Hours for Paid Users

Analyze User Flags and Review Outcomes for Moderation Prioritization

Calculate Total Spend and Identify Key User Metrics

Explore Subscription Patterns and Status Transitions with SQL/Pandas

Analyze Ad CTR and Convert Transactions to USD

Implement Left Join Using Python Dictionaries Efficiently

Clean and Analyze User Transactions with Python Functions

Analyze Streaming Data with SQL for Monthly Trends

Calculate Recent Post Views and Reactions for Social Media

List Departments with Student Counts Including Zero

Retrieve Top Five Ads by Conversions in 30 Days

Calculate Weekly Thread Engagement with Reactions in SQL

Analyze User Purchase Behavior in Online Marketplace Data

Identify Date with Highest Total kWh Consumption Using Pandas

Identify Top Contributors by Recent Post Count

Identify and Flag Bot Traffic in Online Forum

Determine Top Advertisers by Conversion Rate and CTR Analysis

Create Country-Level Spend Report Using Pandas

Analyze Monthly Prime vs Non-Prime Sales and Price Buckets

Aggregate User Activity, Fit Regression, Interpret Coefficients

How to Prepare for SQL Interviews

Master the Core Concepts

Common Patterns You'll See

Practice with Real Company Questions