Data Cleaning and Quality Validation in SQL Questions
Handle NULL values, duplicates, and data type issues within queries. Implement data validation checks (row counts, value distributions, date ranges). Practice identifying and documenting data quality issues that impact analysis reliability.
MediumTechnical
0 practiced
Create an SQL query that identifies outliers in 'user_monthly_spend' per country using the IQR method: compute Q1 and Q3 per country, derive IQR, and flag rows outside [Q1 - 1.5*IQR, Q3 + 1.5*IQR]. Return counts of outliers per country and example user_ids.
HardTechnical
0 practiced
A key product metric in your dashboard suddenly shows 0 for a specific day. You have access to SQL and transformation logic. Outline a prioritized SQL-driven debugging plan to find which transformation produced zeros: include queries to check raw source counts, intermediate staging tables, recent deployments, and join-level filters that could zero-out the metric.
MediumTechnical
0 practiced
Compare dbt test strategies, Great Expectations, and a homegrown SQL-check framework for implementing data quality in a data platform. Evaluate them on maintainability, expressiveness for complex checks, integration with CI/CD, and team ownership. Which would you pick for a mid-size analytics org and why?
HardTechnical
0 practiced
Propose a SQL query that computes a daily data-quality scorecard per table using metrics: null_rate, duplicate_rate, schema_changes, and value_range_violations. Define how you would weight these metrics into a single 0-100 score and show sample output with at least one table/day example.
HardTechnical
0 practiced
Design an SLA-based alerting and runbook system for data quality failures. Include (1) how to pick thresholds to trigger alerts, (2) how to avoid alert fatigue, (3) sample SQL checks for critical metrics, and (4) automated remediation patterns for trivial failures (e.g., transient downstream ingestion lag).
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