Quiz: Transformation and Loading

1. Which of the following is the primary purpose of data normalization during the transformation step of an ETL pipeline?

2. Which pandas function would you typically use to group sales data by month when aggregating data?

3. What is the main advantage of bulk loading data into a SQL database instead of inserting rows one at a time?

4. Which of the following best describes the role of data cleaning in a pipeline?

5. When saving a cleaned DataFrame to a CSV file in pandas, which argument ensures that the index is not written to the file?

Which of the following is the primary purpose of data normalization during the transformation step of an ETL pipeline?

Select the correct answer

To ensure consistency in data format and scale across datasets

To encrypt sensitive data before storage

To join multiple tables into a single dataset

To reduce data volume by removing unnecessary columns

Which pandas function would you typically use to group sales data by month when aggregating data?

Select the correct answer

concat()

merge()

pivot_table()

groupby()

What is the main advantage of bulk loading data into a SQL database instead of inserting rows one at a time?

Select the correct answer

It allows for more complex data transformations.

It reduces the risk of data duplication.

It significantly improves loading performance.

It automatically creates database indexes.

Which of the following best describes the role of data cleaning in a pipeline?

Select the correct answer

Detecting and correcting errors or inconsistencies in the data

Splitting large files into smaller chunks for processing

Removing all columns that have missing values

Encrypting data before loading

When saving a cleaned DataFrame to a CSV file in pandas, which argument ensures that the index is not written to the file?

Select the correct answer

skip_index=True

remove_index=True

no_index=True

index=False

Tout était clair ?

Comment pouvons-nous l'améliorer ?

Merci pour vos commentaires !

Section 3. Chapitre 4

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Quiz: Transformation and Loading

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1. Which of the following is the primary purpose of data normalization during the transformation step of an ETL pipeline?

2. Which pandas function would you typically use to group sales data by month when aggregating data?

3. What is the main advantage of bulk loading data into a SQL database instead of inserting rows one at a time?

4. Which of the following best describes the role of data cleaning in a pipeline?

5. When saving a cleaned DataFrame to a CSV file in pandas, which argument ensures that the index is not written to the file?

Which of the following is the primary purpose of data normalization during the transformation step of an ETL pipeline?

Select the correct answer

To ensure consistency in data format and scale across datasets

To encrypt sensitive data before storage

To join multiple tables into a single dataset

To reduce data volume by removing unnecessary columns

Which pandas function would you typically use to group sales data by month when aggregating data?

Select the correct answer

concat()

merge()

pivot_table()

groupby()

What is the main advantage of bulk loading data into a SQL database instead of inserting rows one at a time?

Select the correct answer

It allows for more complex data transformations.

It reduces the risk of data duplication.

It significantly improves loading performance.

It automatically creates database indexes.

Which of the following best describes the role of data cleaning in a pipeline?

Select the correct answer

Detecting and correcting errors or inconsistencies in the data

Splitting large files into smaller chunks for processing

Removing all columns that have missing values

Encrypting data before loading

When saving a cleaned DataFrame to a CSV file in pandas, which argument ensures that the index is not written to the file?

Select the correct answer

skip_index=True

remove_index=True

no_index=True

index=False

Tout était clair ?

Comment pouvons-nous l'améliorer ?

Merci pour vos commentaires !

Section 3. Chapitre 4