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Updated weekly · Last refresh Aug 30

Cobblestone Energy Data Analyst Interview Questions

The questions to prepare for a Cobblestone Energy Data Analyst interview. Questions from real interview reports rank first. Updated weekly.

34questions
~5htotal time
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1
PipelinesStart here. 7 questions · ~56 min
Handling Missing Data in PipelinesMedium

Approach for handling missing data in an ML data pipeline, including validation, imputation, and safe downstream consumption.

InfrastructureETLBatch ProcessingCobblestone Energy
Optimize Slow Spark ETL PipelineHard

Redesign a slow Databricks Spark ETL pipeline to cut runtime from 3 hours to under 60 minutes without breaking data quality or SLAs.

Cobblestone Energy
Data Cleaning in ETL PipelinesEasy

Approach for cleaning and preparing raw data inside an ETL pipeline.

Data WranglingETLQualityCobblestone Energy
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2
Statistics & Probability3 questions · ~24 min
Solve a Numerical Reasoning QuestionEasy

Tests ability to compute and reason through quantitative inequalities.

VarianceExpected ValueConditional ProbabilityCobblestone Energy
Numerical Reasoning CheckEasy

Tests basic numerical reasoning accuracy.

VarianceExpected ValueConditional ProbabilityCobblestone Energy
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3
Metrics3 questions · ~24 min
Ensuring Data-Driven DecisionsMedium

Tests ability to translate analysis into decisions with clear assumptions and metrics.

KPIsLeading IndicatorsDiagnosisCobblestone Energy
Energy Anomaly InvestigationMedium

Tests structured troubleshooting and validation steps for energy data.

KPIsLeading IndicatorsDiagnosisCobblestone Energy
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4
Behavioral & Leadership20 questions · ~160 min
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5
More topics1 question · ~8 min
SQL Query for Data ExtractionEasy

Tests your SQL proficiency for retrieving and shaping data for analysis.

JoinsGroup ByAggregationsCobblestone Energy
Hands-on SQL practiceWrite and run real queries in the editor. 3 drills · ~30 min
Second Highest Trader SalaryEasy
Practice
Practice drill

Find the second highest distinct salary from a single table using basic PostgreSQL ordering and limiting.

SubqueriesRankingSortingeClinicalWorksInformaticaSpectraforce Technologies
Monthly Sales Trends by CategoryMedium
Practice
Practice drill

Aggregate monthly sales by product category and use LAG to calculate month-over-month changes.

InfrastructureToolsData WranglingTotal Wine & MoreInc.Benjamin Moore
Contact Center Agent Performance MetricsHard
Practice
Practice drill

Compute daily agent call KPIs and SLA using joins, aggregations, and window ranking in a contact center model.

ETLAggregationsData ModelingADP
The finish line: interview-readyComplete all 34 questions plus 3 hands-on drills to finish this plan.