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Welcome to the Advanced Topics in Dimensional Modeling MCQs Page

Dive deep into the fascinating world of Advanced Topics in Dimensional Modeling with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Advanced Topics in Dimensional Modeling, a crucial aspect of Data Warehousing and OLAP. In this section, you will encounter a diverse range of MCQs that cover various aspects of Advanced Topics in Dimensional Modeling, from the basic principles to advanced topics. Each question is thoughtfully crafted to challenge your knowledge and deepen your understanding of this critical subcategory within Data Warehousing and OLAP.

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Check out the MCQs below to embark on an enriching journey through Advanced Topics in Dimensional Modeling. Test your knowledge, expand your horizons, and solidify your grasp on this vital area of Data Warehousing and OLAP.

Note: Each MCQ comes with multiple answer choices. Select the most appropriate option and test your understanding of Advanced Topics in Dimensional Modeling. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Advanced Topics in Dimensional Modeling MCQs | Page 6 of 10

Explore more Topics under Data Warehousing and OLAP

Q51.
Which of the following queries would likely run the fastest in a data warehouse without using aggregate fact tables?

a.

Query 1: Total sales for a single customer for one day

b.

Query 2: Total sales for a single customer for one month

c.

Query 3: Total sales for all customers in a specific territory for one year

d.

Query 4: Total sales for a single product category for one quarter

Discuss
Answer: (a).Query 1: Total sales for a single customer for one day Explanation:Query 1, which involves a single customer and a single day, is likely to run the fastest without using aggregate fact tables.
Q52.
When might aggregate fact tables be most helpful in improving query performance in a data warehouse?

a.

For queries involving a large number of dimensions

b.

For queries involving a small number of dimensions

c.

For queries that require complex arithmetic calculations

d.

For queries with low cardinality attributes

Discuss
Answer: (a).For queries involving a large number of dimensions Explanation:Aggregate fact tables are most helpful for improving query performance for queries involving a large number of dimensions.
Q53.
In a typical data warehouse, what is the main reason for needing detailed data at the lowest level of granularity in the base fact tables?

a.

To improve query performance

b.

To save storage space

c.

To simplify the schema structure

d.

To reduce the number of fact table rows

Discuss
Answer: (a).To improve query performance Explanation:Detailed data at the lowest level of granularity is needed in base fact tables to support various forms of analysis and improve query performance.
Q54.
In a grocery chain data warehouse with 300 stores, 40,000 products, and daily sales data, what is the maximum number of base fact table records at the lowest level of granularity?

a.

2,000,000 records

b.

200,000,000 records

c.

2,000,000,000 records

d.

20,000,000,000 records

Discuss
Answer: (c).2,000,000,000 records Explanation:The maximum number of base fact table records can be calculated as 1825 (days) * 300 (stores) * 4000 (products per store) * 1 (promotion) = 2,000,000,000 records.
Q55.
When running queries in a data warehouse environment, which type of result sets do users typically need?

a.

Result sets involving a single customer or product

b.

Result sets involving raw unprocessed data

c.

Result sets with large numbers of table rows

d.

Result sets comprising a variety of combinations of individual fact table rows

Discuss
Answer: (d).Result sets comprising a variety of combinations of individual fact table rows Explanation:Users in data warehouse environments need result sets comprising a variety of combinations of individual fact table rows to perform different forms of analysis.
Q56.
What is the primary advantage of using aggregate fact tables in data warehousing?

a.

Reduced storage space requirements

b.

Simplified schema structure

c.

Improved data loading performance

d.

Dramatic improvement in query performance

Discuss
Answer: (d).Dramatic improvement in query performance Explanation:The primary advantage of using aggregate fact tables is the dramatic improvement in query performance, as they reduce the number of rows to be retrieved and summarized.
Q57.
What is the primary purpose of aggregate fact tables in a data warehouse?

a.

To eliminate the need for dimension tables

b.

To store the most granular data at the lowest level of granularity

c.

To improve query performance by summarizing data at higher levels of dimension hierarchies

d.

To serve as a backup for the base fact tables

Discuss
Answer: (c).To improve query performance by summarizing data at higher levels of dimension hierarchies Explanation:The primary purpose of aggregate fact tables is to improve query performance by summarizing data at higher levels of dimension hierarchies.
Q58.
In the context of aggregate fact tables, what are "one-way aggregates"?

a.

Aggregates that involve only one dimension hierarchy

b.

Aggregates that involve all dimension hierarchies

c.

Aggregates that reduce the number of dimensions in a fact table

d.

Aggregates that summarize data at the lowest level of granularity

Discuss
Answer: (a).Aggregates that involve only one dimension hierarchy Explanation:"One-way aggregates" are aggregates that involve only one dimension hierarchy, moving up the hierarchy while keeping the other dimensions at their lowest levels.
Q59.
What is the purpose of forming two-way aggregate fact tables in a data warehouse?

a.

To create backups for the base fact tables

b.

To eliminate the need for dimension tables

c.

To improve data loading performance

d.

To summarize data at higher levels of hierarchies in two dimensions

Discuss
Answer: (d).To summarize data at higher levels of hierarchies in two dimensions Explanation:Two-way aggregate fact tables summarize data at higher levels of hierarchies in two dimensions, improving query performance for specific combinations.
Q60.
What is the effect of sparsity on the number of rows in aggregate fact tables?

a.

Sparsity always reduces the number of rows in aggregate fact tables.

b.

Sparsity has no impact on the number of rows in aggregate fact tables.

c.

Sparsity can either increase or decrease the number of rows in aggregate fact tables.

d.

Sparsity increases the number of rows in aggregate fact tables.

Discuss
Answer: (c).Sparsity can either increase or decrease the number of rows in aggregate fact tables. Explanation:Sparsity may either increase or decrease the number of rows in aggregate fact tables depending on the nature of the data being summarized.
Page 6 of 10

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