Data Quality and Anomaly Detection Questions

Focuses on identifying, diagnosing, and preventing data issues that produce misleading or incorrect metrics. Topics include spotting duplicates, missing values, schema drift, logical inconsistencies, extreme outliers caused by instrumentation bugs, data latency and pipeline failures, and reconciliation differences between sources. Covers validation strategies such as data tests, checksums, row counts, data contracts, invariants, and automated alerting for quality metrics like completeness, accuracy, and timeliness. Also addresses investigation workflows to determine whether anomalies are data problems versus true business signals, documenting remediation steps, and collaborating with engineering and product teams to fix upstream causes.

EasySystem Design

75 practiced

You own an hourly ETL pipeline that loads analytics tables. Describe how you would monitor data timeliness, define lateness SLAs, detect regressions in timeliness, and what automated or manual actions to take when a breach occurs to protect downstream reports and models.

MediumTechnical

79 practiced

You see a 4x spike in signups on a Monday. Lay out a step-by-step investigation plan to determine if this is a true business event or a data collection problem. Include checks across traffic sources, instrumentation, cohort analysis, and sample SQL queries or charts you would run.

HardTechnical

72 practiced

Malicious actors or automated bots may inject events to skew metrics or poison training data. Describe how you would detect and isolate malicious data injection, prevent it at ingestion, quarantine affected records, and repair models trained on poisoned data.

MediumTechnical

77 practiced

As a data scientist, how would you convince product and engineering teams to prioritize fixing an upstream data quality bug that reduced a model's accuracy by 5%? Describe how you would build a business case, present trade-offs, and propose a remediation roadmap with measurable milestones.

MediumTechnical

71 practiced

Compare checksums, row counts, and histograms as data validation techniques for detecting ingestion problems. Explain which classes of errors each method detects, where they fail, computational cost at scale, and recommended use cases or combinations for production systems.

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