Data Validation, Leakage Prevention & Statistical Rigor Questions

Data validation and governance practices within data pipelines and analytics platforms, including schema validation, data quality checks, anomaly detection, lineage, and data quality metrics. Addresses leakage prevention in analytics and machine learning workflows (e.g., proper train/test separation, cross-validation strategies, and leakage risk mitigation) and emphasizes statistical rigor in analysis and modeling (experimental design, sampling, hypothesis testing, confidence intervals, and transparent reporting). Applicable to data engineering, analytics infrastructure, and ML-enabled products.

MediumTechnical

0 practiced

An external provider started returning duplicate records and inconsistent IDs mid-stream. Describe immediate mitigations to protect model serving (e.g., dedupe at ingestion, switch to previous trusted dataset snapshot), how to patch ETL to deduplicate and backfill without introducing leakage, and what validations you'd add to detect similar regressions earlier.

HardTechnical

0 practiced

You are responsible for rolling out critical model feature changes across multiple regions with differing data distributions and privacy laws. Create a plan covering feature versioning, per-region validation checks, canary deployments, rollback triggers, and compliance gating. Include how to coordinate teams and what automation you would build to reduce risk.

EasyBehavioral

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Tell me about a time you discovered a data issue that threatened a model's performance or a production decision. Use the STAR framework: describe the Situation, your Task, the Actions you took (diagnosis steps, immediate mitigation), the Result, and what long-term changes you implemented to prevent recurrence.

HardTechnical

0 practiced

For a multivariate time-series forecasting problem with exogenous regressors, design a cross-validation and backtesting harness that prevents leakage and properly evaluates probabilistic forecasts. Include fold creation, handling of exogenous variable availability at prediction time, evaluation metrics for probabilistic forecasts (e.g., CRPS), and calibration checks.

HardTechnical

0 practiced

Propose a method to compute and maintain confidence intervals around streaming model performance metrics (e.g., AUC, precision@k) in production when observations arrive correlated and non-iid. Provide formulas or approximations for standard errors, describe windowing strategies, and discuss efficient approximations to compute CIs under strict latency.

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