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

An A/B test shows identical unexpected uplift in both treatment and control groups. Describe how you would determine whether the issue is in instrumentation, experiment assignment, or metric aggregation. Provide sample SQL checks for verifying randomization, assignment leakage, and metric derivation.

HardSystem Design

0 practiced

Design an architecture that provides low-latency anomaly detection and basic RCA suggestions for streaming feature pipelines processing millions of events per second. Describe sampling strategies, summary statistics to compute in-stream (sketches), how to aggregate and store heavy-weight analytics in batch, and how to present results to SREs and ML engineers for fast triage.

HardTechnical

0 practiced

Explain how to safely evolve nested Avro or Protobuf schemas used in streaming topics when consumers include both legacy and new model versions. Cover schema registry strategies, backward and forward compatibility rules, migration planning, and methods to detect and debug silent consumer failures after a schema evolution.

HardTechnical

0 practiced

A serving model returns wrong predictions two hours after a deploy. The upstream team says no schema changes, but logs show a surge of malformed events starting at time T. Outline a detailed investigation to determine whether malformed events corrupted feature materialization in the feature store, and propose ways to repair the feature store with minimal or no downtime.

HardSystem Design

0 practiced

Design an automated root cause analysis system that, when alerted to a downstream metric spike or drop, traces candidate upstream causes across batch and streaming jobs, ranks likely culprits, and surfaces a prioritized list of investigative actions to an operator. Define the telemetry to collect, how to model lineage, ranking heuristics, and strategies to reduce false positives at scale.

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