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Welcome to the Data Extraction,Transformation and Loading MCQs Page

Dive deep into the fascinating world of Data Extraction,Transformation and Loading with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Data Extraction,Transformation and Loading, a crucial aspect of Data Warehousing and OLAP. In this section, you will encounter a diverse range of MCQs that cover various aspects of Data Extraction,Transformation and Loading, 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 Data Extraction,Transformation and Loading. 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 Data Extraction,Transformation and Loading. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Data Extraction,Transformation and Loading MCQs | Page 10 of 15

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Q91.
In which scenario is the "destructive merge" mode applicable when applying data to a data warehouse table?

a.

Type 1 slowly changing dimensions

b.

Incremental loads

c.

Initial loads

d.

Full refreshes

Discuss
Answer: (a).Type 1 slowly changing dimensions Explanation:The "destructive merge" mode is applicable when applying data to a data warehouse table in scenarios like Type 1 slowly changing dimensions where the existing record must be replaced by the corrected incoming record.
Q92.
How are full refreshes different from initial loads in terms of data application modes?

a.

Full refreshes do not erase existing data, while initial loads replace existing data.

b.

Full refreshes and initial loads use the same data application modes.

c.

Full refreshes add incoming data to existing data, while initial loads load the whole database from scratch.

d.

Full refreshes do not apply data, while initial loads apply data.

Discuss
Answer: (a).Full refreshes do not erase existing data, while initial loads replace existing data. Explanation:Full refreshes involve erasing existing data before applying incoming data, while initial loads replace existing data with incoming data.
Q93.
What are the two methods for maintaining a data warehouse and keeping it up to date after the initial load?

a.

Extraction and transformation

b.

Update and delete

c.

Update and refresh

d.

Full load and incremental load

Discuss
Answer: (c).Update and refresh Explanation:The two methods for maintaining a data warehouse and keeping it up to date after the initial load are "update" and "refresh."
Q94.
When might a full refresh be a better option than an update for keeping a data warehouse up to date?

a.

When more than 25% of the source records change daily

b.

When less than 15% of the source records change daily

c.

When the number of records to be updated is high

d.

When the cost of updating per record is lower

Discuss
Answer: (a).When more than 25% of the source records change daily Explanation:A full refresh may be a better option when more than 25% of the source records change daily.
Q95.
What is the primary reason for converting production system keys to system-generated keys in the data warehouse?

a.

To maintain consistency with source systems

b.

To make data integration easier

c.

To facilitate faster data loading

d.

To prevent data leakage to external parties

Discuss
Answer: (b).To make data integration easier Explanation:Converting production system keys to system-generated keys in the data warehouse helps make data integration easier and ensures that keys used in the data warehouse are consistent.
Q96.
What is the key of the fact table in a data warehouse based on?

a.

The concatenation of keys from fact tables

b.

The concatenation of keys from dimension tables

c.

The concatenation of keys from both fact and dimension tables

d.

The time stamp in the source systems

Discuss
Answer: (c).The concatenation of keys from both fact and dimension tables Explanation:The key of the fact table in a data warehouse is based on the concatenation of keys from both fact and dimension tables.
Q97.
What is the primary function of ETL (Extraction, Transformation, and Loading) in a data warehouse?

a.

Data extraction only

b.

Data transformation only

c.

Data loading only

d.

Data extraction, transformation, and loading

Discuss
Answer: (d).Data extraction, transformation, and loading Explanation:ETL stands for Extraction, Transformation, and Loading, which involves all three functions to prepare and load data into a data warehouse.
Q98.
Why is data extraction for a data warehouse an ongoing function carried out at very frequent intervals?

a.

To reduce data extraction complexity

b.

To ensure a one-time event for data loading

c.

To accommodate changes in source systems

d.

To eliminate the need for data transformation

Discuss
Answer: (c).To accommodate changes in source systems Explanation:Data extraction for a data warehouse is an ongoing function to accommodate changes in source systems and ensure data is up-to-date.
Q99.
What is the key difference between data loading in a data warehouse and data loading for a new operational system?

a.

Data loading for a data warehouse is a one-time event.

b.

Data loading for an operational system involves incremental loads.

c.

Data loading for a data warehouse includes ongoing incremental loads and periodic full refreshes.

d.

Data loading for an operational system does not require data transformation.

Discuss
Answer: (c).Data loading for a data warehouse includes ongoing incremental loads and periodic full refreshes. Explanation:Data loading in a data warehouse includes ongoing incremental loads and periodic full refreshes, unlike a one-time event for an operational system.
Q100.
What role do vendor tools play in the ETL process for data warehousing?

a.

Vendor tools are not suitable for ETL functions.

b.

Vendor tools replace manual methods completely.

c.

Vendor tools offer flexible and suitable solutions for ETL tasks.

d.

Vendor tools are limited to data extraction only.

Discuss
Answer: (c).Vendor tools offer flexible and suitable solutions for ETL tasks. Explanation:Vendor tools provide flexible and suitable solutions for various ETL functions in data warehousing.
Page 10 of 15

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