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

Write SQL for video-call recipients and FR activity

Transform DataFrame and compute diff-in-diff

Train and analyze a classifier

Write SQL to compare social-only vs game-only engagement

Write SQL for influence score and follower growth

Compute video-call SQL metrics with edge cases

Compute reply-based user metrics in 7 days

Write SQL for late-delivery metrics by window

Debug ML pipeline and build text parser

Analyze time-zoned events with pandas

Print the K-th non-empty line

Write SQL for content-view analytics

Write SQL for percent and window changes

Compute ads revenue by geography in SQL

Write SQL for profit, growth, retention

Compute CTR and metrics with pandas

Compute percent of active users with 50+ calls

Recommend two-hop follows in Python

Write SQL for top categories and highly active users

Implement vectorized NumPy ops and explain broadcasting

Find top category by video time spent

Compute C/T metrics from bookings and visits

Debug and harden trial-assignment Python code

Compute active ad revenue by creation source

Write monthly customer and sales SQL queries

Compute percent of first-cancel users who never return

Aggregate D1 retention cohorts in SQL

Parse and build binary data in Python

Solve three SQL problems (easy/medium/hard)

Transform flat keys into nested dictionary

Determine Maximum Consecutive Order Days Per User

Analyze shopping funnel with joins and windows

Explain handling very large datasets

Refactor SQL into an aggregated report

Compute violation rate and flag precision in SQL

Tackle Python tasks under time pressure

Implement paginated API ingestion

Implement scalable word count locally

Measure Late Deliveries and Identify Top Delayed Restaurants

Write SQL to compute campaign net revenue

Compute CTR by pin_format and date

Analyze video flags and reviews with SQL

Compare list/dict; parse JSON/CSV at scale

Compute CTR drop with exclusions

Write SQL for fares and age-band counts

Compute paid subscriber YoY counts by month

Write SQL using HAVING and window functions

Write SQL for retention, conversion, and churn

Write SQL to find top net-revenue products

Write SQL and Python for transaction analytics

How to Prepare for SQL Interviews

Master the Core Concepts

Common Patterns You'll See

Practice with Real Company Questions