
Data Transformation Challenge
Authored by Coke parker
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12th Grade
Used 8+ times

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10 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is an example of a data cleaning technique?
Removing duplicates
Ignoring outliers in the dataset
Converting all text to uppercase
Filling missing values with random data
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Explain the concept of data normalization.
Data normalization involves duplicating data to increase redundancy.
Data normalization is only applicable to text data.
Data normalization is the process of organizing data in a database to reduce redundancy and improve data integrity.
Data normalization is the process of converting data into a different format.
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What are some common data aggregation strategies?
Separating
Categorizing
Grouping, filtering, summarizing, merging
Combining
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
How does data encoding help in data transformation?
Data encoding converts data into audio files.
Data encoding standardizes the data format for efficient processing and communication.
Data encoding randomizes the data for security purposes.
Data encoding slows down data processing.
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Name a popular data transformation tool used in the industry.
Apache Spark
Tableau
TensorFlow
Hadoop
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Describe a data visualization technique.
Using only text to represent data visually
Drawing random shapes without any data correlation
Creating 3D holograms to display data
One data visualization technique involves creating charts, graphs, and maps to visually represent patterns, trends, and relationships within data.
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Why is data cleaning important in the data transformation process?
Data cleaning is unnecessary and adds unnecessary complexity to the process
Data cleaning is important to ensure accuracy, consistency, and reliability of the data for analysis and decision-making.
Data cleaning can introduce errors and inconsistencies without any benefits
Data cleaning only focuses on aesthetics and does not impact analysis
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