Large Dataset Management and Technical Analysis Questions
Develop skills in working efficiently with large datasets: data cleaning and validation, efficient aggregation and manipulation, handling missing data, identifying and managing outliers. Master advanced Excel features or learn SQL for database queries. Practice data quality assessment. Learn efficient workflows that scale with dataset size. Understand data security and privacy considerations.
MediumTechnical
0 practiced
Design a system to detect data drift between the training distribution and production inputs for tabular features. Specify metrics (e.g., PSI, KL divergence, population stability index), sampling cadence, thresholds, alerting behavior, and actions (alert only vs automated rollback or retrain). How would you tune thresholds to avoid false positives?
MediumSystem Design
0 practiced
A 1B-row fact table must be joined with a 10k-row dimension to enrich features in Spark. Describe concrete strategies to optimize the join: broadcast join, repartitioning and bucketing, map-side joins, caching, and use of adaptive query execution. Explain how to choose among them given memory and cluster constraints.
HardTechnical
0 practiced
You're designing a privacy strategy for multiple teams training models on sensitive user attributes. Compare differential privacy (central and local), federated learning with secure aggregation, synthetic data generation, role-based data access, and auditing. For each approach, discuss trade-offs in utility, complexity, and compliance.
MediumTechnical
0 practiced
Explain trade-offs between normalized relational schemas and denormalized wide-feature tables for ML use: query latency, storage duplication, update complexity, referential integrity, and analytics vs ML access patterns. Provide recommendations for offline training and online serving.
MediumTechnical
0 practiced
Write a SQL query to compute weekly retention cohorts: users who first performed 'signup' in week N and returned in subsequent weeks. Use schema: events(user_id, event_type, occurred_at). Produce columns cohort_week, week_offset, users_retained. Explain performance considerations for large tables and how to optimize.
Unlock Full Question Bank
Get access to hundreds of Large Dataset Management and Technical Analysis interview questions and detailed answers.
Sign in to ContinueJoin thousands of developers preparing for their dream job.