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Prep plan
Updated weekly · Last refresh Oct 17

Clickhouse Data Analyst Interview Questions

The questions to prepare for a Clickhouse Data Analyst interview. Questions from real interview reports rank first. Updated daily.

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~4htotal time
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1
Product SenseStart here. 6 questions · ~51 min
Resolve Conflicting Product SignalsHard

How to decide when analytics and user interviews point in different directions.

Trade-offsUser ResearchFeature PrioritizationClickhouse
Define Feature Success MetricsMedium

Framework for choosing a feature's primary success metric and guardrails before launch.

MetricsFeature PrioritizationProduct VisionClickhouse
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2
SQL & Data Manipulation10 questions + 2 drills · ~104 min
Explain SQL Join TypesEasy

Explain INNER, LEFT, RIGHT, FULL OUTER, CROSS, and SELF JOINs with examples and when to use each.

JoinsData WranglingGroup ByClickhouse
Rolling Retention in SQLHard
Practice

Calculate Webex daily active users and seven-day rolling retention using PostgreSQL CTEs, joins, and distinct aggregations.

sql queryClickhouse
Troubleshooting Slow QueriesHard
Practice
Practice drill

Identify repeated slow queries by execution time, scan volume, user, and recent execution history.

database optimizationJoinsData WranglingClickhouse
Schema Design for User ActivityHard
Practice
Practice drill

Design normalized activity tables and query each user's recent activity count and latest event efficiently.

database optimizationschema designquery optimizationAmazon Web Services
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3
Behavioral & Leadership8 questions · ~67 min
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4
More topics4 questions · ~34 min
Design Test for New FeatureMedium

Design an A/B test for a new platform feature, including success metrics, power, guardrails, and a clear ship decision.

experiment designfeature evaluationA/B TestingClickhouse
Diagnose a Sudden DAU DropHard

Investigate whether an 8.2% DAU decline is driven by product breakage, logging issues, or normal seasonal patterns.

DAU/MAUDiagnosisTime SeriesClickhouse
Handle Late Data in StreamingHard

Design a streaming pipeline that can absorb late-arriving events while keeping aggregates correct and downstream tables stable.

Stream ProcessingIdempotencyData ModelingClickhouse
Vanity vs Actionable MetricsEasy

Distinguish vanity metrics from metrics that drive decisions in a growth review.

North Star MetricKPIsDiagnosisClickhouse

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