Worksheetsbig data analysis
Total questions: 10
Worksheet time: 5mins
Name
Class
Date
1.
Which of the following is a primary source of big data?
a)
Data extracted from textbooks
b)
Data collected from scientific journals
c)
Data obtained from social media platforms
d)
Data generated directly from users, devices, and sensors
2.
What is the purpose of data preprocessing in big data analysis?
a)
To increase the complexity of data for analysis
b)
To randomly sample data for analysis
c)
To clean, transform, and organize raw data into a usable format for analysis.
d)
To skip data cleaning and directly analyze raw data
3.
Which of the following is NOT a data visualization technique used in big data analysis?
a)
Bar graph
b)
Scatter plot
c)
Histogram
d)
Pie chart
4.
What is the goal of exploratory data analysis (EDA) in big data analysis?
a)
To summarize main characteristics, gain insights, detect patterns, test assumptions, and develop hypotheses.
b)
To skip data preprocessing steps
c)
To create complex machine learning models
d)
To predict future outcomes based on historical data
5.
Which of the following is a tool used for big data analysis?
a)
TensorFlow
b)
Apache Hadoop
c)
MongoDB
d)
MySQL
6.
What is the role of data scientists in big data analysis?
a)
Data scientists are responsible for marketing strategies in big data analysis
b)
Data scientists only collect data but do not analyze or interpret it
c)
Data scientists collect, process, analyze, and interpret large volumes of data to extract valuable insights and make data-driven decisions.
d)
Data scientists focus on qualitative data analysis only
7.
Which of the following is an example of predictive analytics in big data analysis?
a)
Using social media sentiment analysis to predict weather patterns
b)
Applying statistical models to predict customer churn based on historical data
c)
Utilizing data visualization tools to analyze real-time market trends
d)
Using machine learning algorithms to forecast sales trends based on past sales data
8.
What is the benefit of real-time analytics in big data analysis?
a)
Delayed insights and slower decision-making
b)
Limited data processing capabilities
c)
Increased data security risks
d)
Immediate insights and faster decision-making
9.
Which of the following is a challenge in implementing big data analysis?
a)
Lack of available data
b)
Limited storage capacity
c)
Inadequate processing power
d)
Complexity of managing and processing large volumes of data
10.
How does big data analysis contribute to innovation?
a)
Big data analysis contributes to innovation by limiting access to information.
b)
Big data analysis contributes to innovation by revealing insights and opportunities hidden within large datasets.
c)
Big data analysis contributes to innovation by ignoring data trends and patterns.
d)
Big data analysis contributes to innovation by creating more confusion and complexity.
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