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.

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SQL Interview Questions

SQL Interview Questions

All SQL Questions

Count unconnected posts and reactions

Write SQL for reply-based recipient metrics

Write SQL for repeat churn

Calculate Cohort Retention

Write SQL for call metrics

Find high-value crypto users and top CTR

Write SQL for top categories and highly active users

Compute Cohort Retention Rate

Explain Pandas and SQL Basics

Write SQL for DAU and first-purchase conversion

Analyze advertiser spend by source

Handle repeated churn in SQL

Calculate Pirated Usage and Revenue Loss

Differentiate pandas objects and SQL filters

Analyze video flags and reviews with SQL

Find high-value crypto users and top-CTR product

Query departments and top earners

Train and analyze a classifier

Pivot daily users and revenue by platform

Debug row loss after SQL joins

Find top category by video time spent

Write SQL for monthly spend and ratios

Find least active countries

Write SQL for seller and vehicle metrics

Compute article-type diversity per user and histogram

Write SQL to compute signup and retention lift

Write SQL for call analytics

Aggregate D1 retention cohorts in SQL

Write SQL for percent and window changes

Write SQL window functions for D7 retention

Debug ML pipeline and build text parser

Write Call Analytics SQL Queries

Find recommended friend pairs by shared songs

Debug and harden trial-assignment Python code

Debug SQL join that drops rows

Explain pandas and SQL basics

Write SQL for video-call recipients and FR activity

Write SQL and Python for transaction analytics

Analyze spend cohort and source shifts

Find top-paid employee per department

Analyze Multiple-Account Users in SQL

Write SQL for CTR and revenue

Compute this-year spend share of last-year whales

Detect sessions and gaps using SQL LEAD

Implement a nested object validator

Compute each member’s current notification status

Convert Dictionary to DataFrame

Compute percent of first-cancelled users who never rebook

Transform DataFrame and compute diff-in-diff

Write SQL for rankings, state, and aggregations

How to Prepare for SQL Interviews

Master the Core Concepts

Common Patterns You'll See

Practice with Real Company Questions