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

0 practiced

Explain how segmenting data by dimensions such as user cohort, device, region, or client version helps isolate the root cause of a metric regression. Provide concrete examples of SQL or Spark aggregation patterns you would run to identify the most impacted segment and how you would prioritize segments to debug first.

HardSystem Design

0 practiced

Design a multi-region safe backfill process to correct miscomputed feature values for models serving traffic across regions. Consider data locality, large data movement costs, cross-region consistency, and how to serve corrected values progressively without disrupting predictions or violating latency SLAs.

MediumTechnical

0 practiced

You discover that a categorical feature's mapping upstream changed (new labels, renaming). Describe how you would detect this issue during both model training and serving, how it can impact one-hot encoders or embedding layers, and outline a safe remediation and rollout strategy including temporary remapping and logging.

MediumTechnical

0 practiced

Describe how you would communicate a prioritized incident involving erroneous model predictions to stakeholders including product, analytics, ML, SRE, and affected customers. What information should be included in the initial alert, in-status updates, and in the final postmortem to ensure clarity and accountability?

HardTechnical

0 practiced

Propose a metadata schema to capture for each dataset and job to enable rapid root cause analysis. Include fields such as source URI, job id, commit hash, schema fingerprint, partition ranges, consumer list, last successful run, and recent data quality metrics. Explain storage choices and how you would make this metadata easily queryable at scale.

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