Technical Problem Solving and Learning Agility Questions

Evaluates a candidates ability to diagnose and resolve technical challenges while rapidly learning new technologies and concepts. Topics include systematic troubleshooting approaches, root cause analysis, debugging strategies, how the candidate breaks down ambiguous problems, and examples of self directed learning such as studying new frameworks, libraries, or application programming interfaces through documentation, courses, blogs, or side projects. Also covers intellectual curiosity, baseline technical comfort, the ability to learn from peers and feedback, and collaborating with engineers to understand architectures and tradeoffs. Interviewers may probe how the candidate acquires new skills under time pressure, transfers knowledge across domains, and applies new tools to deliver outcomes.

MediumTechnical

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How would you measure statistical significance in BI when comparing conversion rates across segments with different sample sizes? Provide concrete tests or formulas you would use (e.g., z-test, chi-square, confidence intervals) and mention any practical adjustments when sample sizes are small.

MediumTechnical

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Explain how approximate distinct counting algorithms like HyperLogLog work at a high level, and describe when you'd choose HyperLogLog instead of exact COUNT(DISTINCT) in dashboards. Discuss mergeability, error characteristics, and storage trade-offs.

MediumTechnical

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Explain differences between INNER JOIN, LEFT JOIN, and SEMI-JOIN. For a BI query where a large fact table needs to be filtered by a relatively small dimension of 'active promotions', describe when using a semi-join (existence check) can improve performance and why.

HardTechnical

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You're evaluating an A/B test where control and treatment have different user arrival patterns (treatment users tend to be early in the day). How would you detect sample bias, adjust analysis, and communicate uncertainty? List statistical tests, diagnostics (covariate balance, time-based stratification), and remediation options.

MediumBehavioral

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A colleague's exploratory dashboard runs a heavy live query every morning at 9am and slows the warehouse. How would you approach resolving this while balancing technical solutions and interpersonal dynamics? Outline concrete steps: measurement, coordination, technical mitigations, and how you'd propose a change without creating friction.

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