Cloud Data Warehouse Design and Optimization Questions

Covers design and optimization of analytical systems and data warehouses on cloud platforms. Topics include schema design patterns for analytics such as star schema and snowflake schema, purposeful denormalization for query performance, column oriented storage characteristics, distribution and sort key selection, partitioning and clustering strategies, incremental loading patterns, handling slowly changing dimensions, time series data modeling, cost and performance trade offs in cloud managed warehouses, and platform specific features that affect query performance and storage layout. Candidates should be able to discuss end to end design considerations for large scale analytic workloads and trade offs between latency, cost, and maintainability.

EasyTechnical

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Define distribution keys (distribution strategy) and sort/clustering keys in distributed columnar warehouses (e.g., Redshift, Snowflake). Give simple examples of how choosing a poor distribution or sort key harms join or aggregation performance and how to fix it.

HardTechnical

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Discuss advanced storage layout choices for analytical data: Parquet vs ORC, ideal file size tuning for cloud object storage, compaction strategies, and how these choices affect downstream query engines or cloud warehouses (bytes scanned, metadata overhead, and parallelism).

HardTechnical

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Explain causes and consequences of data skew in distributed query engines. Provide detailed mitigation strategies such as broadcasting small tables, redistributing joins, key salting, splitting hot keys, and adaptive query plans. For each strategy, describe trade-offs and when it is appropriate.

HardTechnical

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Design a policy and technical implementation for protecting PII in a cloud data warehouse across Snowflake, BigQuery, and Redshift. Cover encryption (at rest/in transit), tokenization/masking strategies, column/row-level access controls, anonymization approaches for analytics, key management, and audit logging to demonstrate compliance.

MediumTechnical

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Explain partition pruning and design a partitioning scheme in BigQuery or Snowflake for a fact table that is queried mostly by event_date and device_type. Specify partition field/granularity, clustering columns (if applicable), and how pruning reduces bytes scanned and cost.

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