1. Which technology is commonly used for handling large-scale data processing in parallel?
a) SQL
b) Hadoop
c) Python
d) MATLAB
Answer: b) Hadoop
Explanation: Hadoop is a popular open-source framework for distributed storage and processing of large datasets across clusters of computers using simple programming models.
2. What is the primary purpose of data discovery in Big Data analytics?
a) Storing large datasets
b) Processing real-time data
c) Exploring and understanding data
d) Creating predictive models
Answer: c) Exploring and understanding data
Explanation: Data discovery involves exploring datasets to uncover patterns, trends, and insights that can inform decision-making processes.
3. Which of the following is an open-source technology commonly used for Big Data Analytics?
a) Oracle
b) Microsoft Excel
c) Apache Spark
d) Tableau
Answer: c) Apache Spark
Explanation: Apache Spark is an open-source distributed computing system that is commonly used for big data analytics due to its speed and ease of use.
4. How does cloud computing contribute to Big Data analytics?
a) By reducing data volume
b) By limiting data access
c) By providing scalable infrastructure
d) By increasing data security
Answer: c) By providing scalable infrastructure
Explanation: Cloud computing allows organizations to scale their computational resources dynamically based on the demands of their data analytics tasks, enabling them to handle large volumes of data more efficiently.
5. Which analytics approach focuses on predicting future trends and behaviors based on historical data?
a) Descriptive analytics
b) Diagnostic analytics
c) Predictive analytics
d) Prescriptive analytics
Answer: c) Predictive analytics
Explanation: Predictive analytics involves analyzing historical data to identify patterns and trends and make predictions about future events or behaviors.
6. How does Mobile Business Intelligence leverage Big Data?
a) By restricting data access on mobile devices
b) By enabling real-time data analysis on mobile devices
c) By reducing the need for data storage
d) By limiting data sharing capabilities
Answer: b) By enabling real-time data analysis on mobile devices
Explanation: Mobile Business Intelligence allows users to access and analyze large datasets in real-time using mobile devices, facilitating quick decision-making even when away from traditional computing resources.
7. What does Crowd Sourcing Analytics involve?
a) Analyzing data from social media platforms
b) Utilizing input from a large group of users
c) Restricting data access to a specific crowd
d) Analyzing data collected from crowds at events
Answer: b) Utilizing input from a large group of users
Explanation: Crowd Sourcing Analytics involves collecting data or insights from a large group of people, often through online platforms, to gather diverse perspectives or to solve complex problems.
8. What is the primary goal of Inter- and Trans-Firewall Analytics?
a) Enhancing firewall security
b) Bypassing firewalls
c) Analyzing data across multiple firewalls
d) Implementing additional firewalls
Answer: c) Analyzing data across multiple firewalls
Explanation: Inter- and Trans-Firewall Analytics involves analyzing data that resides across multiple firewall-protected networks to gain insights and detect potential security threats or anomalies.
9. Which aspect of Information Management is crucial for effective Big Data analytics?
a) Restricting data access
b) Data quality and governance
c) Reducing data volume
d) Limiting data sharing
Answer: b) Data quality and governance
Explanation: Data quality and governance ensure that the data used for analytics is accurate, reliable, and compliant with regulations, thereby enhancing the effectiveness and reliability of analytical insights.
10. How does Hadoop’s parallel processing capability benefit Big Data analytics?
a) By reducing data variety
b) By speeding up data processing
c) By limiting data accessibility
d) By increasing data security
Answer: b) By speeding up data processing
Explanation: Hadoop’s parallel processing capability allows for the distributed processing of large datasets across multiple nodes, significantly speeding up data processing and analysis tasks.