Pro80 ProblemsSQL + Python
AdTech SQL & Python Interview Questions
Advertising technology companies process billions of ad events daily — impressions, clicks, conversions, and revenue signals. These SQL and Python challenges are modeled after real analytics work at Google, Meta, Amazon Ads, The Trade Desk, TikTok, Microsoft Advertising, Criteo, Snap, Pinterest, DoubleVerify, and more. Practice attribution modeling, campaign ROI measurement, audience segmentation, CTR & CPM analysis, bid strategy optimization, and cross-channel reporting.
Companies working in AdTech
These practice problems are modeled after the kind of data and analytics challenges teams in this industry typically face.
Company names and logos are trademarks of their respective owners, used here only to describe the kind of data these companies work with. Let's Data Science is not affiliated with, endorsed by, or sponsored by any company shown. Practice problems are original works and are not real interview questions from these companies. Rights & takedowns.
Difficulty Distribution
Easy
14
18% of problems
Medium
29
36% of problems
Hard
31
39% of problems
Expert
6
8% of problems
What You'll Practice
Campaign performance metrics
Attribution modeling
Funnel conversion analysis
Audience segmentation
Revenue & ROAS optimization
CTR & CPM calculations
Cross-channel reporting
Bid strategy analysis
Topics Covered
SQL· 9
aggregationbasic queries filteringcleaning transformdate timejoinsscenario sqlset operationssubqueries cteswindow functions
Python· 12
eda statisticsfeature engineeringpandas aggregationpandas applypandas basicspandas cleaningpandas datetimepandas filteringpandas mergingpandas reshapingpandas scenariopandas window
All Problems80 total
01
Active Search Campaigns by BudgetPro
SQLEasy02Approved Video Creative AssetsPro
SQLEasy03US Mobile Impressions Excluding BotsPro
SQLEasy04Expensive Clicks With Poor Landing PagesPro
SQLMedium05Active Campaigns With Advertiser IndustryPro
SQLEasy06Ads With Campaign Type and Asset TypePro
SQLMedium07Clicks With Impression ViewabilityPro
SQLMedium08Conversions With Click and Campaign DetailsPro
SQLMedium09Creative Assets Never Used in AdsPro
SQLHard10Full Funnel: Clicks to ConversionsPro
SQLHard11Total Spend by Campaign (Dollars)Pro
SQLEasy12Impressions by Country and DevicePro
SQLMedium13Invalid Click Rate by Campaign TypePro
SQLMedium14Top 5 Advertisers by Total SpendPro
SQLHard15Campaign KPI Summary (CTR, CPC, CPM)Pro
SQLHard16Rank Campaigns by Spend per AdvertiserPro
SQLMedium17Daily Running Total SpendPro
SQLMedium18Top 2 Expensive Clicks per CampaignPro
SQLHard19Campaign CTR With 7-Day Moving AvgPro
SQLHard20Conversion Value Percentile by TypePro
SQLHard21Campaigns Above Average SpendPro
SQLMedium22Ads With Clicks But No ConversionsPro
SQLHard23Advertisers With High Credit UtilizationPro
SQLHard24Campaign ROAS by Attribution ModelPro
SQLExpert25Campaigns Launched in Last 30 DaysPro
SQLEasy26Monthly Impression Volume by PlacementPro
SQLMedium27Avg Days to Conversion by Month and TypePro
SQLHard28Impressions With Normalized Device and CostPro
SQLEasy29Creative Assets With Size CategoryPro
SQLMedium30Clicks With CPC Tier and Quality BucketPro
SQLHard31Campaigns: Clicks But No ConversionsPro
SQLMedium32Users Who Saw AND Clicked Same CampaignPro
SQLMedium33Campaign Health ScorecardPro
SQLHard34Campaign Funnel Drop-Off AnalysisPro
SQLHard35Cross-Device Attribution & Fraud AuditPro
SQLExpert36Active Advertiser ProfilesPro
PYTHONEasy37Campaign Objective CountsPro
PYTHONEasy38Ad Group Device Targeting SummaryPro
PYTHONMedium39Creative Asset Approval SummaryPro
PYTHONMedium40Active Search CampaignsPro
PYTHONEasy41High-Cost US ImpressionsPro
PYTHONMedium42Campaigns by Objective in Date RangePro
PYTHONMedium43Valid Clicks with Quality ThresholdPro
PYTHONHard44Count Campaigns by StatusPro
PYTHONEasy45Total Daily Spend by CampaignPro
PYTHONMedium46Average Quality Score by Click TypePro
PYTHONMedium47Impression Stats by Country and DevicePro
PYTHONHard48Advertiser Performance SummaryPro
PYTHONHard49Campaigns with Advertiser NamesPro
PYTHONEasy50Clicks with Campaign DetailsPro
PYTHONMedium51Creative Assets Not Used in AdsPro
PYTHONMedium52Click Path with AdvertiserPro
PYTHONHard53Ad Hierarchy DetailsPro
PYTHONHard54Rank Campaigns by Spend per AdvertiserPro
PYTHONMedium55Running Total of Daily Spend per CampaignPro
PYTHONMedium567-Day Moving Average CTR per CampaignPro
PYTHONHard57Daily Spend Change vs Previous DayPro
PYTHONHard58Campaign Start Month and YearPro
PYTHONEasy59Campaign Duration in DaysPro
PYTHONMedium60Monthly Impression Volume by PlacementPro
PYTHONHard61Fill Missing Campaign End DatesPro
PYTHONEasy62Normalize Impression Costs to USDPro
PYTHONMedium63Standardize Device Types Across EventsPro
PYTHONHard64Pivot Impressions by Country and DevicePro
PYTHONMedium65Pivot Campaign Spend by Objective and TypePro
PYTHONHard66Classify Campaigns by Budget TierPro
PYTHONMedium67Campaign Health Score ClassificationPro
PYTHONHard68Compute Daily CTR and CPCPro
PYTHONMedium69Campaign Spend Quartile BucketingPro
PYTHONHard70Time-Based Impression FeaturesPro
PYTHONHard71Campaign Feature MatrixPro
PYTHONExpert72Click Quality FeaturesPro
PYTHONHard73Campaign Budget Descriptive StatsPro
PYTHONMedium74Spend vs Conversions RankingPro
PYTHONHard75Anomalous Daily Spend DetectionPro
PYTHONHard76Campaign Performance ScorecardPro
PYTHONHard77Advertiser ROI ReportPro
PYTHONHard78Full-Funnel Attribution ReportPro
PYTHONExpert79Campaign Health Classification SystemPro
PYTHONExpert80Cross-Device Conversion AnalysisPro
PYTHONExpertReady to practice AdTech?
80 SQL and Python challenges built from real adtech data. Graded instantly in your browser — no setup required.