Data Organization and Infrastructure Challenges Questions

Demonstrate knowledge of the technical and operational problems faced by large scale data and machine learning teams, including data infrastructure scaling, data quality and governance, model deployment and monitoring in production, MLOps practices, technical debt, standardization across teams, balancing experimentation with reliability, and responsible artificial intelligence considerations. Discuss relevant tooling, architectures, monitoring strategies, trade offs between innovation and stability, and examples of how to operationalize models and data products at scale.

MediumTechnical

0 practiced

A production model exhibits intermittent increases in inference latency. Outline the diagnostic steps you would take, what telemetry you would collect (request traces, CPU/GPU metrics, queue lengths), and short-term mitigations you might apply to keep latency within SLA.

HardTechnical

0 practiced

You inherit a model with many ad-hoc preprocessing scripts spread across repos and pipelines. Propose a plan to reduce technical debt by refactoring into shared, tested components while minimizing production risk and downtime. Provide migration phases and validation steps.

HardTechnical

0 practiced

Design metrics and instrumentation to detect upstream data pipeline issues that silently corrupt features (examples: timezone shift, unit changes, new nulls). Specify which invariants to track at feature-level and how to aggregate alerts to reduce noise.

HardSystem Design

0 practiced

Design a multi-region, low-latency model serving architecture that must provide median inference latency under 50ms and handle 5k QPS per region. Include model distribution, cache strategy, consistency considerations, failover, and strategies for A/B and canary testing.

EasyTechnical

0 practiced

List common sources of technical debt in ML systems (examples: ad-hoc feature engineering, embedded business rules in code, untracked data dependencies). For each source propose a practical mitigation or remediation step a team can implement within 3 months.

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