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
0 practiced
For multi-region data where business date must align to a specific business timezone, design a validation strategy to ensure daily aggregates computed in UTC match those computed when converting event times to the business timezone, especially across DST boundaries. Include SQL checks and test cases.
MediumTechnical
0 practiced
You maintain dbt models that transform raw events into user metrics. Describe a concrete set of dbt tests (built-in and custom) you would add to CI to validate uniqueness, not_null, accepted_range, and daily freshness for a `users_metrics` model. Explain how these tests help protect downstream dashboards.
EasyTechnical
0 practiced
Explain what a data contract is between a producer and consumer (for example, between a product team that emits events and analytics). Describe three elements that should be in a data contract to reduce validation work and a simple enforcement mechanism a data analyst could put in place.
EasyTechnical
0 practiced
List the checks you would run on an incoming CSV before you import it into the data warehouse. Include at least eight specific checks (e.g., column count, header names, date parseability, allowed enums, duplicate primary keys, encoding issues) and explain briefly why each matters for downstream analysis.
MediumTechnical
0 practiced
Design a SQL-based test that verifies join cardinality invariants between `customers` and `transactions` such that a join should not multiply customers by more than a known factor (e.g., each customer expected 0-100 transactions). Provide the query and describe actions when the invariant is violated.
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