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
List potential numeric precision and rounding bugs that can silently affect financial metrics (integer overflow, implicit casting, floating point rounding, timezone-induced day splits). For each, give a concrete test case and how you'd detect and prevent it in SQL and visualization layers.
HardTechnical
70 practiced
Describe an algorithm to compute approximate quantiles (e.g., median, 95th percentile) efficiently for very large distributed datasets without moving all raw data to a single node. Explain trade-offs between accuracy and communication cost and suggest technologies or libraries you would use.
HardTechnical
73 practiced
You must estimate the cardinality of unique users in a streaming events pipeline with strict memory limits. Describe how HyperLogLog works at a high level, its accuracy/memory trade-offs, and how you'd integrate HLL into nightly aggregates while ensuring safety for business metrics that care about exact counts occasionally.
EasyTechnical
66 practiced
Explain what input validation means in the context of data ingestion pipelines and why server-side validation matters for a data analyst working with production datasets. Provide three concrete examples of failures that can happen if validation is only client-side (e.g., malformed dates, wrong types, injected payloads) and list three specific checks you'd implement at the ingestion layer (e.g., datatype enforcement, allowed-value lists, schema conformity).
HardSystem Design
95 practiced
Architect a fail-safe strategy for near-real-time analytics where events may arrive late or out of order. Requirements: minimize double counting, allow incremental updates, enable reprocessing windows, and give stakeholders confidence in near-real-time KPIs. Sketch components, policies for watermarking, and mechanisms to correct aggregates when late data arrives.
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