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Data Warehousing (DW) is a process of collecting and managing data from varied sources to provide meaningful business insights. A Data warehouse is typically used to connect and analyze business data from heterogeneous sources. For data analysis and reporting, the data warehouse is the core of the BI system.

It is a blend of technologies and components which aids the strategic use of data. Furthermore, it is a process of transforming data into information and making it available to users in a timely manner to make a difference.

Data Warehousing MCQ

1.What is a data warehouse?

2.What does non-volatile mean in the context of a data warehouse?

3.What is the key difference between OLTP and OLAP workloads?

4.What is a star schema?

5.How does a snowflake schema differ from a star schema?

6.What does a fact table typically contain?

7.What is the grain of a fact table?

8.What is a surrogate key?

9.What does a Type 1 slowly changing dimension do?

10.What does a Type 2 slowly changing dimension do?

11.What is a conformed dimension?

12.What distinguishes ETL from ELT?

13.What is a staging area used for in a warehouse pipeline?

14.What is a data mart?

15.What is the main risk of a data lake without governance?

16.What is a lakehouse architecture?

17.Why are columnar storage formats efficient for analytical queries?

18.What is partitioning in a data warehouse?

19.What is clustering or sort keys used for in a warehouse table?

20.What is a materialised view?

21.What is an aggregate table used for?

22.What is change data capture (CDC)?

23.Why is idempotency important in a data pipeline?

24.What is a late-arriving fact?

25.What is data lineage?

26.What is the purpose of a data dictionary in a warehouse?

27.What is a degenerate dimension?

28.What is a junk dimension?

29.What is a factless fact table?

30.What does data quality testing in a pipeline typically check?

31.What is the purpose of an orchestration tool such as Airflow?

32.What is dbt primarily used for?

33.What is a slowly changing dimension Type 3 approach?

34.Why should surrogate keys be used rather than natural keys in a dimensional model?

35.What is the primary reason to keep raw source data after loading?

36.What does a bridge table resolve?

37.What is the risk of loading a warehouse directly from a production database during business hours?

38.What does incremental loading mean?

39.Why is the date dimension almost always included in a dimensional model?

40.What is the main governance benefit of a single governed warehouse layer?

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