Meta Data Scientist Data Manipulation (SQL/Python) Interview Questions
Practice 145 real Data Manipulation (SQL/Python) interview questions for Data Scientist roles at Meta.
"10 years of experience but never worked at a top company. PracHub's senior-level questions helped me break into FAANG at 35. Age is just a number."
"I was skeptical about the 'real questions' claim, so I put it to the test. I searched for the exact question I got grilled on at my last Meta onsite... and it was right there. Word for word."
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"I've used LC, Glassdoor, and random Discords. Nothing comes close to the accuracy here. The questions are actually current — that's what got me. Felt like I had a cheat sheet during the interview."
"The solution quality is insane. It covers approach, edge cases, time complexity, follow-ups. Nothing else comes close."
"Legit the only resource you need. TC went from 180k -> 350k. Just memorize the top 50 for your target company and you're golden."
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"Literally just signed a $600k offer. I only had 2 weeks to prep, so I focused entirely on the company-tagged lists here. If you're targeting L5+, don't overthink it."
"Coaches and bootcamp prep courses cost around $200-300 but PracHub Premium is actually less than a Netflix subscription. And it landed me a $178K offer."
"I honestly don't know how you guys gather so many real interview questions. It's almost scary. I walked into my Amazon loop and recognized 3 out of 4 problems from your database."
"Discovered PracHub 10 days before my interview. By day 5, I stopped being nervous. By interview day, I was actually excited to show what I knew."
"I recently cleared Uber interviews (strong hire in the design round) and all the questions were present in prachub."
"The search is what sold me. I typed in a really niche DP problem I got asked last year and it actually came up, full breakdown and everything. These guys are clearly updating it constantly."
Count unconnected posts and reactions
You are analyzing a newly launched feed feature intended to improve engagement by showing more unconnected content. Assume the following tables: - pos...
Write SQL for reply-based recipient metrics
You work on a social product and are given two tables. Assumptions (use these unless you state otherwise): - All timestamps are in UTC. - A “reply” is...
Count heavy callers in 7 days
You are given two tables. users - user_id BIGINT PRIMARY KEY - country_code STRING calls - call_id BIGINT PRIMARY KEY - caller_id BIGINT - recipient_i...
Count Recent High-Volume Call Users
Given the following tables: users - user_id BIGINT - country STRING - is_active BOOLEAN calls - call_id BIGINT - initiated_at TIMESTAMP - caller_id BI...
Write SQL for multi-account metrics
A consumer app allows one user to own multiple accounts. Use SQL to answer the following questions. Assume the database has these tables: accounts - a...
Analyze spend and creation-source shifts
You are working with ads data. Assume the following tables, with all timestamps interpreted in UTC. - advertisers(advertiser_id BIGINT, advertiser_cat...
Analyze spend cohort and source shifts
You work on an ads platform. Assume all timestamps are in UTC. Interpret last year as calendar year 2023 and this year as calendar year 2024. Tables: ...
Write SQL for CTR and Revenue
You are given the following tables: ads( ad_id BIGINT, advertiser_id BIGINT, ad_type VARCHAR, -- values include direct and brand ad...
Compute High-Call Usage Rates
You are given two tables for a voice-calling product: users - user_id BIGINT - country_code STRING calls - call_id BIGINT - caller_id BIGINT - recipie...
Find least active countries
You are given an ads activity table. Table: ad_activity - ad_id BIGINT — unique ad identifier - advertiser_id BIGINT — advertiser that owns the ad - c...
Analyze advertiser spend by source
You are given two tables: advertisers - advertiser_id BIGINT - advertiser_type VARCHAR — examples: smb, enterprise, agency, internal - status VARCHAR ...
Calculate CTR and ad revenue
This interview included two SQL tasks. Task 1: Compare CTR during peak and non-peak hours You are given three tables: - ads(ad_id BIGINT, advertiser_i...
Write SQL for Pixel Signal Metrics
You are working on Meta Ads Pixel analytics. Assume all timestamps are stored in UTC, and analyze the last 30 complete calendar days. Tables 1. advert...
Write SQL to compare social-only vs game-only engagement
You are given two tables capturing Oculus app usage. Define an 'active day' as a UTC date on which a user generates at least one event. Consider only ...
Write SQL for video-call recipients and FR activity
Given the schema and samples below, write ANSI‑SQL to answer both questions. Assume dates are stored in UTC. Today is 2025-09-01, so “yesterday” is 20...
Write SQL for CTR and revenue
Write SQL for the following two tasks. Problem 1: CTR during peak vs. non-peak hours You are given three tables: - ads(ad_id BIGINT, advertiser_id BIG...
Compute CTR for peak vs non-peak hours
You work on a video ads platform. You are given three tables and asked to compare CTR during peak hours vs non-peak hours. Tables Assume the following...
Compute this-year spend share of last-year whales
Context You work on an ads analytics dataset. Assume all timestamps are in UTC, and “last year” / “this year” refer to the previous/current calendar y...
Write SQL for seller and category metrics
Assume the following marketplace tables. Table: listing_interactions - buyer_id STRING - seller_id STRING - product_id STRING - interaction_date DATE ...
Compute cohort GMV and payer rate with edge cases
You are given the following schema (timestamps are UTC): users(user_id INT, country STRING, created_at TIMESTAMP) events(user_id INT, event_ts TIMESTA...