Data Validation and Anomaly Detection Questions
Techniques for validating data quality and detecting anomalies using SQL: identifying nulls and missing values, finding duplicates and orphan records, range checks, sanity checks across aggregates, distribution checks, outlier detection heuristics, reconciliation queries across systems, and building SQL based alerts and integrity checks. Includes strategies for writing repeatable validation queries, comparing row counts and sums across pipelines, and documenting assumptions for investigative analysis.
MediumTechnical
0 practiced
Explain strategies to handle schema evolution in analytic warehouses (columns added/removed/renamed) so validation checks don't break pipelines. Provide three concrete patterns and a SQL example to detect when an expected column disappears.
HardSystem Design
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Design monitoring metrics for the health of the validation checks themselves: what to measure (coverage, runtime, flakiness, false-positive rate), how to compute them in SQL, and how to visualize these metrics for both SRE and analytics audiences.
MediumTechnical
0 practiced
You need to detect duplicates in a very large table (hundreds of millions of rows). Describe a SQL-based approach using window functions and partitioning, and explain physical optimizations (indexes, cluster keys, partitioning) you would recommend for production-scale deduplication.
MediumTechnical
0 practiced
Write an ANSI SQL script that produces a daily 'data_quality_summary' table with columns: table_name, run_date, null_ratio_pct, duplicate_count, row_count, min_ts, max_ts. Assume you have a metadata table listing tables and primary key columns; show how you'd iterate or generate the queries.
HardTechnical
0 practiced
Investigation exercise: Your product analytics shows a 30% drop in DAU for a specific country. Provide detailed SQL checks you would run to determine whether this is due to data ingestion, filtering/joins, user segmentation, timezone issues, or a real product problem. List queries and expected indicators for each hypothesis.
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