Dimensional Modeling and Star Schema Concepts Questions
Understand fact and dimension tables, surrogate keys, and slowly changing dimensions. Be able to write queries that efficiently query dimensional data structures. Understand grain of fact tables and how to aggregate appropriately.
HardTechnical
27 practiced
A BI report queries a star join across a large fact and several wide dimensions and runs out of memory. Propose architectural and schema changes (e.g., star flattening, aggregate tables, projection) and show a sample rewritten SQL or architecture diagram that mitigates memory pressure while preserving analytical fidelity.
MediumTechnical
25 practiced
Write a SQL query (choose your dialect) that finds the most recent dimension version (SCD Type 2) for each fact row efficiently, given:Return orders with the correct product_key as of order_date.
orders_fact(order_id, product_business_key, order_date)
product_dim(product_key, product_business_key, effective_from, effective_to, current_flag)MediumTechnical
21 practiced
In a distributed ETL environment you must generate surrogate keys for dimension rows that are inserted by multiple parallel workers. Describe strategies to generate surrogate keys safely and scalably (e.g., central sequences, UUIDs, composite keys) and discuss pros/cons for analytics workloads.
HardTechnical
24 practiced
Multiple business teams disagree on the definition of 'active user'. As a data analyst responsible for dimensional models and metrics, design a process and schema approach to resolve conflicting definitions, implement versioned metrics, and provide lineage so teams can choose the appropriate metric for their dashboards.
HardTechnical
19 practiced
You need to migrate reporting from a normalized OLTP schema to a star schema without downtime. Outline a migration plan covering data modeling, ETL changes, validation strategy, cutover steps, and rollback plan. Include steps to ensure reports remain accurate during migration.
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