1. What is the primary advantage of Hadoop’s parallel processing framework?
a) Reduced data storage costs
b) Increased data security
c) Enhanced data processing speed
d) Improved data visualization
Answer: c) Enhanced data processing speed
Explanation: Hadoop’s parallel processing framework enables the distribution of large datasets across multiple nodes, allowing for simultaneous data processing. This parallelism significantly accelerates data processing speed compared to traditional sequential processing methods.
2. Which technology is commonly used for data discovery in Big Data analytics?
a) Apache Hadoop
b) Apache Spark
c) Tableau
d) MongoDB
Answer: c) Tableau
Explanation: Tableau is a widely used data visualization tool that facilitates data discovery by allowing users to explore, visualize, and understand data insights intuitively through interactive dashboards and visualizations.
3. What is a characteristic of open-source technologies for Big Data analytics?
a) Proprietary licensing
b) Limited scalability
c) Restricted customization
d) Community-driven development
Answer: d) Community-driven development
Explanation: Open-source technologies for Big Data analytics are characterized by community-driven development, where software is developed collaboratively by a global community of developers. This model promotes transparency, innovation, and rapid evolution of the technology.
4. How does cloud computing benefit Big Data analytics?
a) Decreases data accessibility
b) Increases infrastructure costs
c) Enables scalability and flexibility
d) Reduces data security
Answer: c) Enables scalability and flexibility
Explanation: Cloud computing provides on-demand access to a scalable and flexible infrastructure, allowing organizations to easily scale their Big Data analytics resources up or down based on fluctuating demands. This scalability and flexibility enhance agility and cost-effectiveness in handling large volumes of data.
5. What is the primary objective of predictive analytics in Big Data?
a) Analyzing historical data
b) Making informed predictions
c) Generating descriptive reports
d) Summarizing real-time data
Answer: b) Making informed predictions
Explanation: Predictive analytics in Big Data aims to leverage historical and real-time data to forecast future outcomes or trends. By analyzing patterns and relationships within data sets, predictive analytics enables organizations to make informed decisions and anticipate future events.
6. Which aspect of business intelligence focuses on analyzing data from mobile devices?
a) Spatial analytics
b) Predictive analytics
c) Mobile business intelligence
d) Social media analytics
Answer: c) Mobile business intelligence
Explanation: Mobile business intelligence involves the analysis of data from mobile devices, such as smartphones and tablets, to derive insights and support decision-making processes. It enables users to access and interact with business data on-the-go, enhancing operational efficiency and responsiveness.
7. What does Crowd Sourcing Analytics involve?
a) Analyzing data from social media platforms
b) Utilizing crowdsourced data for analysis
c) Analyzing data from IoT devices
d) Leveraging user-generated content for insights
Answer: b) Utilizing crowdsourced data for analysis
Explanation: Crowd Sourcing Analytics involves harnessing the collective intelligence of a crowd or community to gather, analyze, and interpret data. Organizations leverage crowdsourced data from diverse sources to gain insights, solve problems, and make data-driven decisions.
8. What does Inter- and Trans-Firewall Analytics focus on?
a) Analyzing data across different cloud platforms
b) Analyzing data security breaches
c) Analyzing data from multiple network segments
d) Analyzing data from encrypted sources
Answer: c) Analyzing data from multiple network segments
Explanation: Inter- and Trans-Firewall Analytics involves the analysis of data from various network segments, including internal and external networks separated by firewalls. It aims to uncover insights and anomalies across different segments to enhance network security and performance.
9. Which aspect of Information Management focuses on data governance and compliance?
a) Data integration
b) Data quality management
c) Data security
d) Master data management
Answer: c) Data security
Explanation: Data security within Information Management encompasses strategies, technologies, and practices implemented to protect data from unauthorized access, breaches, and cyber threats. It involves enforcing access controls, encryption, and compliance with regulatory requirements to safeguard sensitive information.
10. What is the primary goal of Information Management in the context of Big Data?
a) Maximizing data storage
b) Minimizing data processing
c) Ensuring data availability
d) Optimizing data utilization
Answer: d) Optimizing data utilization
Explanation: The primary goal of Information Management in the context of Big Data is to optimize data utilization by ensuring that data is efficiently captured, stored, processed, and analyzed to derive actionable insights and drive informed decision-making processes.