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.

MediumTechnical

0 practiced

Explain strategies to make data-related bugs reproducible for debugging and testing. Include techniques such as deterministic sampling, seeding random operations, snapshotting raw inputs, building minimal failing datasets, and packaging environment and code versions to ensure the same failure can be reproduced locally or in CI.

MediumSystem Design

0 practiced

Design a testing strategy for an ML feature pipeline to integrate with CI/CD. Describe unit tests for transforms, integration tests for the pipeline, data regression tests comparing feature statistics to a golden baseline, and performance tests. Be specific about mocked inputs, sample sizes, and failure modes to catch.

MediumTechnical

0 practiced

Write a Python function using numpy and scipy that compares two numeric histograms, a baseline and a current sample, and returns whether they are significantly different. Specify which statistical test you chose and why, describe handling of large arrays and zero-count bins, and explain assumptions and complexity. You do not need to provide fully runnable code but outline the function signature and core steps.

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

How would you instrument dataset and job lineage for a complex ML pipeline that spans Kafka topics, Spark jobs, and a data warehouse so you can quickly trace which upstream change caused a wrong prediction? Describe the metadata to capture, tools or standards you would use, and how you would query lineage during an incident.

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