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MORSE Corp Interview Questions

The questions to prepare for MORSE Corp interviews, across all roles. Questions from real interview reports rank first. Updated weekly.

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1
A/B Testing & ExperimentationStart here. 3 questions · ~26 min
Common Pitfalls in Experiment ResultsHard

Identify the main pitfalls that can distort A/B test interpretation and explain how to guard against them.

PeekingNovelty EffectSample Ratio MismatchMMORSE Corp
Set Up a Valid A/B TestHard

Design an A/B test with a clear control group, valid randomization, and protections for internal validity.

ExperimentationStatistical SignificanceA/B TestingMMORSE Corp
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 TestingMMORSE Corp
2
Behavioral & Leadership6 questions · ~51 min
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3
More topics5 questions · ~43 min
Handling Missing and Dirty SQL DataMedium

Explain how to profile, clean, and standardize missing or dirty data before analysis.

Data WranglingCase WhenQualityMMORSE Corp
First Checks for Metric DropsEasy

Outline the first checks to diagnose a sudden drop in a core product metric, starting with data quality, scope, and decomposition.

Lagging IndicatorsLeading IndicatorsDiagnosisMMORSE Corp
Statistical Significance in Hypothesis TestingEasy

Explain what statistical significance means and why it matters when interpreting experimental or analytical results.

Hypothesis TestingData AnalysisStatistical SignificanceMMORSE Corp
Define Metrics for New FeaturesMedium

Define a success metric for a new feature that captures real user value, not just raw usage.

MetricsFeature Prioritizationuser valueMMORSE Corp
Statistical vs Practical SignificanceMedium

Explain why a statistically significant experiment result may still be too small to matter for product or business decisions.

Confidence IntervalsExperimentationHypothesis TestingMMORSE Corp

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Hands-on SQL practiceWrite and run real queries in the editor. 3 drills · ~30 min
Normalize Messy CRM Contact DataHard
Practice
Practice drill

Clean inconsistent CRM contacts by joining source tables, standardizing values, and flagging bad records.

Data WranglingCase WhenQualityAlphaSense
Validate Monthly Revenue Data QualityHard
Practice
Practice drill

Use joins, a CTE, and CASE logic to flag messy monthly order data and produce cleaned revenue by month.

Data WranglingCase WhenQualityLiterati
Profile Incomplete Customer RecordsEasy
Practice
Practice drill

Classify Best Buy customer records as Complete, Incomplete, or Inconsistent using CASE and null/blank checks.

Data WranglingCase WhenQualityBest Buy
The finish line: interview-readyComplete all 14 questions plus 3 hands-on drills to finish this plan.