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Data Warehouse General Concepts
Author : Akshat Goyal
Name: *  
1. In Star Schema Dimension tables are:
Short and Fat
Long and Thin
Long and Fat
Short and thin
2. The data in Data Warehouse is generally:
Clean Data
Dirty Data
Clean and Dirty Data
None of above
3. (Choose two)
Ralph Kimball believes that portions of data can be combined based on relevance of data and can be used for reporting
Inmon believes that portions of data can be combined based on relevance of data and can be used for reporting
Inmon believes that DW is built and should be used for reporting.
Ralph Kimball believes that DW is built and should be used for reporting.
4. In which type of SCD(Slowly changing dimensions) do we preserve history of data:
Type One
Type Two
Type Three
None of above
5. During ETL load we generally have
Unsorted data for Aggregator
Sorted data for Aggregator
Does not matter if we use Sorted or Unsorted data for Aggregation
6. Sequence of jobs to load data in to warehouse
First load data into fact tables then dimension tables, then Aggregates if any
First load data into dimension tables, then fact tables, then Aggregates if any
First Aggregates then load data into dimension tables, then fact tables
Does not matter if we load either of fact, dimensions, or aggregates
7. Snowflaking means
Normalizing the data
Denormalizing the data
None of Above
8. Drill Across generally use the following join to generate report:
Self Join
Inner Join
Outter Join
9. In general data in Data Warehousing is :
Normalized
Denormalized
None of Above
10. Consolidated data mart is:
First level data mart
Second level data mart
All of these
None of Above
11. In datamarts stovepipe means:
Similar Data
Isolated data
None of Above
12. In 4 step dimensional process, declaring grain of business process is:
First Step
Second Step
Third Step
Fourth Step
13. Your developers asked you to create an index on the PROD_ID column of the SALES_HISTORY table, which has 100 million rows.
The table has approximately 2 million rows of new data loaded on the first day of every month. For the remainder of the month, the table is only queried. Most reports are generated according to the PROD_ID, which has 96 distinct values.
Which type of index would be appropriate?
Bitmap
Reverse key
Unique B-Tree
Normal B-Tree
Function based
Non-unique concatenated
14. Centipede fact table means:
Fact table with no dimensions
Factless fact table
Fact table with two or three dimensions
Fact table with to many dimensions
15. Dimensions are Confirmed when:
They are different
They are either same or one is subset of another
When they can be compared mathematically
None of these
16. Degenerate Dimensions(DD):
Transaction Number, bill of lading number, invoice number may be DD.
DD has no attributes
DD does not join to actual dimension table
None of these
A, B, C are correct.
17. You need to create an index on the SALES table, which is 10 GB in size. You want your index to be spread across many tablespaces, decreasing contention for index lookup, and increasing scalability and manageability.
Which type of index would be best for this table?
bitmap
unique
partitioned
reverse Key
single column
function-based
18. Which type of table is usually created to enable the building of scalable applications, and is useful for large tables that can be queried or manipulated using several processes concurrently?
Regular table
clustered table
partitioned table
index-organized table
19. You need to enforce these two business rules:
1. No two rows of a table can have duplicate values in the specified column.
2. A column cannot contain null values.
Which type of constraint ensures that both of the above rules are true?
Check
Unique
Not null
Primary Key
Foreign key
 
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