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

Walk through a hypothesis-driven root cause analysis approach when daily active users dropped 30% overnight. How do you generate hypotheses, design cheap fast tests to confirm or reject them, how do you gather evidence, and what criteria make you escalate to a full incident response?

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.

EasyTechnical

0 practiced

Give examples of subtle schema changes that can silently break downstream metrics such as changing a field from nullable to non-nullable, altering an enum value, or changing timestamp format. For each example, explain how you would detect the change automatically and list mitigation steps to reduce its impact on production models or reports.

EasyTechnical

0 practiced

Define the core dimensions of data quality that matter for ML pipelines, for example accuracy, completeness, timeliness, consistency, uniqueness, and validity. For each dimension, describe one concrete example of how a failure would manifest during model training or production prediction (including an observable signal), and describe a simple detection check you would implement to catch that failure early.

HardTechnical

0 practiced

You detect a 50% drop in a critical feature's cardinality that feeds an embedding layer, causing model instability. Describe a step-by-step root cause analysis tracing Kafka partitions, transformation stages, deduplication logic, and the feature store. Also propose immediate mitigations to stabilize predictions while you fix the upstream problem.

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