Data Quality and Validation Questions

Covers the core concepts and hands on techniques for detecting, diagnosing, and preventing data quality problems. Topics include common data issues such as missing values, duplicates, outliers, incorrect labels, inconsistent formats, schema mismatches, referential integrity violations, and distribution or temporal drift. Candidates should be able to design and implement validation checks and data profiling queries, including schema validation, column level constraints, aggregate checks, distinct counts, null and outlier detection, and business logic tests. This topic also covers the mindset of data validation and exploration: how to approach unfamiliar datasets, validate calculations against sources, document quality rules, decide remediation strategies such as imputation quarantine or alerting, and communicate data limitations to stakeholders.

MediumTechnical

0 practiced

Design a set of data quality checks to add to a training pipeline to detect label leakage (features derived from the label), severe class imbalance shifts, and duplicate training examples. Explain how you'd implement each check and a threshold policy for blocking training jobs.

MediumTechnical

0 practiced

You receive JSON payloads from multiple vendors where date fields appear in multiple formats (ISO, MM/DD/YYYY, epoch). In Python, design a robust validator/normalizer function normalize_dates(records) that detects formats, normalizes to UTC ISO8601, flags unparseable dates, and returns normalized data plus an error summary.

MediumTechnical

0 practiced

Your production model's metric dropped by 8% this week. Outline a triage plan to determine whether the cause is data quality, code changes, or model drift. List prioritized checks (data snapshot comparisons, schema diffs, feature distributions, recent deployments) and the tools/queries you'd run first.

HardSystem Design

0 practiced

Design an end-to-end data quality platform for an AI organization to validate both training and inference data at petabyte scale. Requirements: support batch + streaming, per-feature monitors, lineage, alerting, quarantine, cost constraints, multi-tenant isolation, and automated report generation. Provide a component diagram and rationale.

HardTechnical

0 practiced

You're on-call for a production model and see a sudden performance drop. You have access to recent data snapshots, feature distributions, model inputs, and logs. Provide a prioritized forensic checklist (top 10 steps) and give the exact queries or plots you'd run first to triage whether the issue is data-related.

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