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Question 1 of 10
1. Question
What are the four main types of dimension columns for proper dimensional data warehouse design?
Correct
The four main building blocks of proper dimensional data warehouse design are: “Surrogate Key”, Columns that identify a dimension member in the data warehouse. “Alternate key”, Columns that identify a dimension member in the LOB application. “Attributes”, Columns that are used as column and row hears, slicers and filters in reports tat provide context to measures. “Metadata”, Columns that track data warehouse processes, data lineage and auditing.
Incorrect
The four main building blocks of proper dimensional data warehouse design are: “Surrogate Key”, Columns that identify a dimension member in the data warehouse. “Alternate key”, Columns that identify a dimension member in the LOB application. “Attributes”, Columns that are used as column and row hears, slicers and filters in reports tat provide context to measures. “Metadata”, Columns that track data warehouse processes, data lineage and auditing.
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Question 2 of 10
2. Question
Which of the following statements best describes Alternate keys:
Correct
Alternate keys are also known as business keys or candidate keys, they are used to identify the dimension members in a LOB application database system.
Incorrect
Alternate keys are also known as business keys or candidate keys, they are used to identify the dimension members in a LOB application database system.
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Question 3 of 10
3. Question
Which of these is NOT a reason to use alternate keys as the data warehouse primary keys?
Correct
Type 2 Slowly Changing Dimension (SCD) is the requirement to track changes of a dimension member over time. Alternate keys would not be able to uniquely identify a specific instance of a dimension member and all its corresponding transactions over time.
Incorrect
Type 2 Slowly Changing Dimension (SCD) is the requirement to track changes of a dimension member over time. Alternate keys would not be able to uniquely identify a specific instance of a dimension member and all its corresponding transactions over time.
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Question 4 of 10
4. Question
In order to improve user experience, hierarchies are defined, these allow business users to arrange data in a logical order by using a predefined object. An example of such a hierarchy would be a natural hierarchy. Which of the following is NOT an example of a natural hierarchy?
Correct
In natural hierarchies, each attribute is related to another in a natural order. For example, in a one-to-many relationship, dates are related to a month, months to a quarter and quarters to a year.
Incorrect
In natural hierarchies, each attribute is related to another in a natural order. For example, in a one-to-many relationship, dates are related to a month, months to a quarter and quarters to a year.
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Question 5 of 10
5. Question
There are three most common methods used to handle changes in dimension values, Type 0, Type 1, and Type 2 SCD (Slowly Changing Dimension). Which of the following statements correctly defines Type 0 SCD?
Correct
Type 0 SCD ignores changes to attribute values, i.e., the original dimension values persist regardless of changes over time. For example a customer’s date of birth.Type 1 SCD replicates the change from the source system in the data warehouse by overwriting the current attribute value. Type 2 SCD maintains a record of both old and new values in separate meta-data columns, storing the start and end dates the record is valid for.
Incorrect
Type 0 SCD ignores changes to attribute values, i.e., the original dimension values persist regardless of changes over time. For example a customer’s date of birth.Type 1 SCD replicates the change from the source system in the data warehouse by overwriting the current attribute value. Type 2 SCD maintains a record of both old and new values in separate meta-data columns, storing the start and end dates the record is valid for.
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Question 6 of 10
6. Question
Fact tables are used in star and snowflake schemas to reference dimension tables primary key column values. The degree of relationship between tables is classified into 5 main types, which of the following is NOT a category?
Correct
The 5 main types of relationship between tables in a data warehouse are: Zero-to-One (0:1), One-to-One (1:1), One-to-Many (1:M), Many-to-Many (M:M), Self-referencing.
Incorrect
The 5 main types of relationship between tables in a data warehouse are: Zero-to-One (0:1), One-to-One (1:1), One-to-Many (1:M), Many-to-Many (M:M), Self-referencing.
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Question 7 of 10
7. Question
Fact table measures the process of “how many” or “how much” in any given business process. Which of the following are 4 types of columns fact tables usually contain?
Correct
Fact tables usually contain four types of columns: “Primary Key”, Column(s) that determine the uniqueness of the table, “Foreign Keys”, Columns that reference a primary column of a dimension, “Measures”, Columns that contain data that can be quantified numerically (in most cases), which can be aggregated. “Metadata”, Columns that track data warehouse processes, data lineage and auditing.
Incorrect
Fact tables usually contain four types of columns: “Primary Key”, Column(s) that determine the uniqueness of the table, “Foreign Keys”, Columns that reference a primary column of a dimension, “Measures”, Columns that contain data that can be quantified numerically (in most cases), which can be aggregated. “Metadata”, Columns that track data warehouse processes, data lineage and auditing.
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Question 8 of 10
8. Question
Measures are columns that contain numerically quantifiable data which can be aggregated. These are further divided into three types, which of the following are the three types of measures?
Correct
The three types of measures are: additive/fully-aggregatable, semi-additive/semi-aggregatable and non-additive/non-aggregatable facts.
Incorrect
The three types of measures are: additive/fully-aggregatable, semi-additive/semi-aggregatable and non-additive/non-aggregatable facts.
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Question 9 of 10
9. Question
One of the types of measures is additive, which of the following correctly defines and provides an example of additive measures?
Correct
Additive measures are numeric values that can be added/summed by all the dimensions in the fact table, such as Sales Amount. All the values can be numerically added in Sales Amount. Whereas semi-additive measures are numeric values that can be added/summed by some of the dimensions in the fact table, such as Account Balance. Adding all the values in an account for every month would not make sense, as it records it separately for each month.
Incorrect
Additive measures are numeric values that can be added/summed by all the dimensions in the fact table, such as Sales Amount. All the values can be numerically added in Sales Amount. Whereas semi-additive measures are numeric values that can be added/summed by some of the dimensions in the fact table, such as Account Balance. Adding all the values in an account for every month would not make sense, as it records it separately for each month.
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Question 10 of 10
10. Question
In a traditional data warehouse, the workload consists of load process executions (ETL) and scheduled data loads via extract, followed by OLAP cube processing and SQL Server Analysis Services (SSAS). Indexes are often used to improve these workloads, from the following options, choose the main types of indexes that are part of every data warehouse index solution.
Correct
The main types of indexes are rowstore and columnstore indexes. “Rowstore indexes” view data and store table horizontally based on rows of data. “Columnstore indexes” view data and store table vertically based on column values.
Incorrect
The main types of indexes are rowstore and columnstore indexes. “Rowstore indexes” view data and store table horizontally based on rows of data. “Columnstore indexes” view data and store table vertically based on column values.