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

Your company spends $X/month on GPU instances for model training. Propose a set of practical strategies to reduce cloud training costs (target 30% reduction) without materially increasing training time. Consider tooling, scheduling, instance choices, data pipelines, and model-level optimizations.

HardTechnical

0 practiced

Implement consistent hashing in Python for routing keys (e.g., feature lookups) across N shards such that when the number of shards changes, minimal keys are remapped. Provide code for ring construction, adding/removing nodes, and mapping a key to a node. Explain how virtual nodes improve balance.

HardBehavioral

0 practiced

Behavioral (senior-level): You need executive approval and budget to modernize core data infrastructure to reduce model error and improve reliability, but ROI is uncertain in the short term. How would you prepare and present a proposal to gain buy-in? Include metrics to track, pilot scope, risk mitigation, and stakeholder engagement plan.

EasyTechnical

0 practiced

List and justify 6 practical data-quality metrics you would monitor for incoming ML training data streams (both batch and streaming). For each metric, explain how it signals potential problems and a simple remediation or alerting strategy.

MediumTechnical

0 practiced

Write Spark SQL (or PySpark DataFrame code) to compute a per-user 7-day rolling average of `purchase_amount` using window functions. The input table `purchases(user_id STRING, amount DOUBLE, event_date DATE)` may have multiple purchases per day. Show sample input and expected output for one user.

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