This data warehousing quiz covers the basic elements of data warehousing, ETL process, and architecture for mid-level professionals.

Data Mining and Warehousing | Free MCQs & Practice Quiz

A star schema simplifies query logic by keeping dimension tables denormalized.
This reduces joins and improves performance in BI tools.
Which of the following is a direct advantage of using a star schema?

OLTP (Online Transaction Processing) systems are designed for day-to-day operations with frequent inserts and updates.
In contrast, OLAP systems are optimized for:

Hybrid approach – star for reports, snowflake for product hierarchy

You’re designing a data warehouse for an e-commerce platform. The sales fact table needs fast reporting, but product details are deeply structured and reused across reports.
What approach offers the best balance of performance and normalization?

Fact tables usually contain foreign keys to dimensions and measurable metrics like sales or revenue.
Which of the following is NOT a characteristic of a fact table?

Less flexibility for parallel transformations

Requires powerful warehouse compute for transformation

ELT (Extract-Load-Transform) is becoming more common with modern cloud warehouses like BigQuery or Snowflake.
Unlike ETL, data is loaded first and transformed inside the warehouse.
What is a key trade-off with ELT?

Which schema is more normalized: star or snowflake?

Which type of table stores quantitative data in a data warehouse?
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Central fact table with surrounding dimension tables

Which of the following is a key characteristic of a star schema?

Increases data volume and improves drill-down insights

A high-granularity fact table logs every product scan in a retail store.
What is the impact of high granularity on performance and data size?

Add data validation + transformation rules before load

Your ETL job failed while transforming data due to null values in a non-nullable field.
What is the best mitigation strategy?

Ensures data is clean and formatted before storing

In a classic ETL pipeline, data is first extracted from source systems, then transformed to match warehouse schema, and finally loaded into the data warehouse.
Why is transforming before loading preferred in ETL?

You want to analyze sales across time, region, and product.
Which OLAP operation helps you focus only on the “North” region while preserving dimensions?

In data warehousing, Slowly Changing Dimensions (SCD) are used to manage changes in dimension attributes like customer address.
Which SCD type keeps both old and new records for historical tracking?

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