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

SQL Interview Questions

All SQL Questions

Write SQL for reply-based recipient metrics

Count Recent High-Volume Call Users

Write SQL for video-call recipients and FR activity

Write SQL for repeat churn

Calculate Pirated Usage and Revenue Loss

Write SQL for monthly spend and ratios

Debug ML pipeline and build text parser

Transform DataFrame and compute diff-in-diff

Debug and harden trial-assignment Python code

Write SQL to compute signup and retention lift

Write SQL for CTR and Revenue

Pivot daily users and revenue by platform

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

Write SQL for late-delivery metrics by window

Compute Fitness App DAU

Implement vectorized NumPy ops and explain broadcasting

Compute video-call SQL metrics with edge cases

Write SQL for percent and window changes

Find high-value crypto users and top CTR

Handle repeated churn in SQL

Analyze time-zoned events with pandas

Implement a nested object validator

Write SQL for content-view analytics

Calculate Cohort Retention

Compute reply-based user metrics in 7 days

Write SQL for retention, conversion, and churn

Compute C/T metrics from bookings and visits

Query US-based users and most active forums

Train and analyze a classifier

Write SQL for influence score and follower growth

Compute monthly signups, conversion, and YoY growth

Write SQL for cuisine median delivery times

Implement paginated API ingestion

Measure Late Deliveries and Identify Top Delayed Restaurants

Compute daily active ads

Find high-value crypto users and top-CTR product

Compute monthly CRR with merges and gaps

Write SQL for DAU and first-purchase conversion

Find orders from bottom-quartile revenue restaurants

Analyze document collaboration patterns

Determine Maximum Consecutive Order Days Per User

Write SQL for content view metrics by device

Refactor SQL into an aggregated report

Write SQL for fares and age-band counts

Recommend two-hop follows in Python

Analyze member video posting behavior by country

Find top-5 most similar rows across datasets

Aggregate D1 retention cohorts in SQL

Compute unread and multi-account user percentages

Tackle Python tasks under time pressure

How to Prepare for SQL Interviews

Master the Core Concepts

Common Patterns You'll See

Practice with Real Company Questions