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Data Quality Debugging and Root Cause Analysis Questions

Focuses on investigative approaches and operational practices used when data or metrics are incorrect. Includes techniques for triage and root cause analysis such as comparing to historical baselines, segmenting data by dimensions, validating upstream sources and joins, replaying pipeline stages, checking pipeline timing and delays, and isolating schema change impacts. Candidates should discuss systematic debugging workflows, test and verification strategies, how to reproduce issues, how to build hypotheses and tests, and how to prioritize fixes and communication when incidents affect downstream consumers.

EasyTechnical
43 practiced
A key dashboard metric dropped 30% compared to yesterday and product stakeholders are alarmed. Describe a step-by-step triage workflow you would run as a data scientist to identify whether this is a data-quality issue, model problem, or true business change. List fast checks, medium-depth checks, and deep-dive checks and the order you would run them.
HardTechnical
34 practiced
Case study: A billing metric was inflated 4x for a 2-hour window and downstream customers were invoiced incorrectly. You have logs, job run metadata, and dataset snapshots. Describe how you would build a timeline, perform root cause analysis, decide whether to backfill, and communicate with business and customers. Include immediate mitigations and long-term fixes.
MediumTechnical
59 practiced
A metric drop only affects a 5% user cohort in one country. Describe how you'd reproduce the issue locally and in a staging environment, the filters and joins you'd check, and how you'd validate whether the problem is caused by data ingestion, transformation, or a downstream aggregation bug.
HardSystem Design
39 practiced
Design a system to version datasets and enable reproducible training: include storage choices for snapshots, metadata catalog, hooks for capturing training inputs, and a mechanism to rehydrate a dataset for retraining while controlling storage costs.
HardTechnical
48 practiced
Implement an algorithm (describe in Python pseudocode) that computes lateness of events using event_ts and ingestion_ts, aggregates percent-late per 1-minute tumbling window with a watermark tolerance of N seconds, and emits alerts when percent-late exceeds a threshold. Assume out-of-order arrivals and bounded lateness.

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