Validation and Edge Case Handling Questions
Focuses on validating data correctness and robustness across application and data layers, and on identifying and handling boundary conditions. Topics include input validation and sanitization, server side validation and schema checks, null and missing value behavior, duplicate and cartesian join issues, off by one and boundary testing, date range and type mismatch handling, and test strategies for edge cases. Emphasizes designing systems and queries that fail safely, produce meaningful errors, and include checks that protect aggregations and joins from corrupt or unexpected data.
HardTechnical
82 practiced
Implement (or outline) a Python module to generate reproducible synthetic datasets for unit and integration tests. Requirements: seedable RNG, control over missing values, out-of-range values, duplicate keys, and skewed distributions. Explain how to integrate such datasets into CI and how to ensure they cover rare edge cases.
MediumTechnical
77 practiced
Define a small set of operational data quality KPIs for production datasets: completeness, accuracy, timeliness (freshness), and consistency. For each KPI propose a measurement method (SQL or metric), a suggested threshold for alerting, and a practical remediation action when thresholds are breached.
MediumTechnical
67 practiced
When binning a continuous variable into quantiles for modeling, describe edge cases (ties at bin edges, skewed distributions, extremely repeated values) and tests that would catch them. Provide a robust binning implementation approach (quantile-based with tie handling or adaptive bins) and how you would unit test it.
EasyTechnical
95 practiced
Explain input validation and sanitization in the context of a data science ingestion pipeline that receives CSV and JSON files from external partners. Describe concrete checks you would perform (schema, data types, required fields, value ranges, length limits, regex for identifiers, and detection of malicious payloads). Explain why server-side validation is critical even if the client performs checks.
MediumTechnical
71 practiced
Propose a lightweight governance checklist for data validation in pull requests (PRs) that modify ETL SQL or feature code: include unit test requirements, sample-size tests, schema compatibility checks, and a smoke test against a staged dataset. How would you automate enforcement in CI?
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