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WorksheetsBI Finals
Total questions: 45
Worksheet time: 23mins
What does SNA stand for in the context of social network analysis?
Structured Network Assessment
Social Network Algorithm
Social Network Analysis
System Network Analysis
What do nodes represent in a network according to SNA?
Entities
Interactions
Clusters
Edges
Which centrality measure identifies influential nodes based on the number of direct connections?
Eigenvector Centrality
Closeness Centrality
Degree Centrality
Betweenness Centrality
What does network density indicate about a network?
Connectivity level
Number of edges
Number of nodes
Tightness of connections
What does the clustering coefficient measure in a network?
Edge density
Node clustering tendency
Network efficiency
Node centrality
What is the process of identifying groups of nodes within a network that are more connected to each other than to the rest of the network?
Community Detection
Network Segmentation
Group Identification
Cluster Analysis
Which method for community detection uses the eigenvalues and eigenvectors of matrices derived from the network?
Label Propagation Algorithm
Girvan-Newman Algorithm
Spectral Clustering
Modularity Optimization
What is the Louvain Method used for in community detection?
Optimizing modularity
Identifying outliers
Calculating centrality
Measuring network density
Which tool is known for network visualization and analysis, featuring interactive visualization and community detection plugins?
Gephi
Cytoscape
NetworkX
Pajek
What is a best practice in SNA and community detection related to data preprocessing?
Adding noise to data
Inconsistent data handling
Ensuring data quality
Ignoring irrelevant information
Which application area involves studying the spread of diseases through social networks using SNA?
Marketing
Epidemiology
Information Science
Organizational Studies
What does the Girvan-Newman Algorithm do in community detection?
Assigns labels to nodes
Removes edges with high betweenness centrality
Identifies functional modules
Optimizes modularity
Which method is used to identify communities by searching for adjacent cliques that share nodes?
Modularity Optimization
Girvan-Newman Algorithm
Clique Percolation Method
Spectral Clustering
What is a key concept in SNA that measures how close a node is to all other nodes in the network?
Eigenvector Centrality
Closeness Centrality
Betweenness Centrality
Degree Centrality
Which field involves analyzing citation networks and co-authorship networks using SNA?
Social Sciences
Epidemiology
Marketing
Information Science
What does the term 'Volume' refer to in Big Data?
The uncertainty and trustworthiness of data
The amount of data generated every second
The different types of data
The speed at which data is generated
Which of the following is a characteristic of Big Data related to the speed of data processing?
Variety
Velocity
Volume
Veracity
What is the main focus of Predictive Analytics in Big Data?
Summarizing historical data
Providing recommendations for actions
Predicting future outcomes
Understanding why something happened
Which technology is a scalable object storage service provided by AWS for Big Data?
MongoDB
Apache Hadoop
Amazon S3
Hadoop Distributed File System (HDFS)
What is the main challenge related to ensuring the accuracy, completeness, and reliability of data in Big Data?
Scalability
Data Quality
Data Integration
Data Security and Privacy
Which application of Big Data involves analyzing patient data for improved diagnosis and personalized treatments?
Finance
Retail
Healthcare
Telecommunications
What is the main future trend in Big Data that involves the convergence of big data and AI/ML?
AI and Machine Learning Integration
Data Lakes
Edge Computing
Real-Time Analytics
Which data processing framework in Big Data provides fast in-memory processing capabilities?
Apache Hadoop
Apache Spark
MongoDB
Apache Flink
What is the main focus of Descriptive Analytics in Big Data?
Predicting future outcomes
Understanding why something happened
Summarizing historical data
Providing recommendations for actions
Which NoSQL database is a distributed, high-performance, and highly scalable database designed to handle large amounts of data?
Cassandra
HBase
MongoDB
Amazon S3
What is the main goal of Big Data Analytics?
To visualize data using Tableau only
To store data using Amazon S3 only
To process and analyze large and varied data sets to uncover hidden patterns and business information
To gather data from social media only
Which component of Big Data Analytics involves processing large datasets using frameworks like Apache Hadoop?
Data Collection
Data Storage
Data Analysis
Data Processing
What type of analytics explains why something happened by identifying patterns and relationships in the data?
Diagnostic Analytics
Descriptive Analytics
Predictive Analytics
Prescriptive Analytics
Which tool is used for creating interactive visualizations and dashboards in Big Data Analytics?
Tableau
Apache NiFi
Hadoop Distributed File System (HDFS)
Apache Spark
In which industry can Big Data Analytics be used for fraud detection and risk management?
Retail
Manufacturing
Finance
Healthcare
What is a challenge in Big Data Analytics related to ensuring the accuracy, completeness, and reliability of data?
Data Integration
Scalability
Data Security and Privacy
Data Quality
Which best practice in Big Data Analytics involves establishing policies and procedures to ensure data quality, security, and privacy?
Data Integration
Data Governance
Automation
Scalable Architecture
What is the main goal of using Apache Spark in Big Data Analytics?
Scalable and fault-tolerant storage
In-memory processing capabilities
Document-oriented database
Automating data collection
Which type of analytics uses historical data to predict future outcomes?
Diagnostic Analytics
Descriptive Analytics
Predictive Analytics
Prescriptive Analytics
Which industry can benefit from Big Data Analytics for network optimization and customer churn prediction?
Government
Healthcare
Manufacturing
Telecommunications
What does Augmented Analytics involve?
Automating data preparation with AI
Developing mobile apps
Using VR technology
Creating physical reports
How does Natural Language Processing (NLP) impact BI/BA?
Converting data into images
Transforming BI/BA by enabling users to query data using natural language
Enabling users to query data using Morse code
Providing data in binary format
What is the focus of Data Democratization?
Making data accessible to more people across organizations
Restricting data access to a few individuals
Encrypting all data
Deleting all data
How does the integration of Big Data and IoT benefit organizations?
It slows down data processing
It generates less data for analysis
It confuses decision-making processes
It enables organizations to gain deeper insights into operations
What is the difference between predictive and prescriptive analytics?
Prescriptive analytics predicts the past
They are the same
Predictive analytics uses historical data to forecast future outcomes, while prescriptive analytics suggests actions based on those predictions
Predictive analytics uses future data to forecast historical outcomes
What is the focus of Ethical and Responsible AI in BI/BA?
Hiding data from users
Using data without permission
Ensuring ethical and responsible use of data
Ignoring ethical considerations
How has Cloud-Based BI Solutions revolutionized BI/BA?
By slowing down data access
By making data inaccessible
By offering scalable, cost-effective solutions that provide real-time access to data from anywhere
By limiting data storage
What is Continuous Intelligence?
Intelligence that is delayed
Real-time analytics that enable organizations to respond immediately to changing conditions
Intelligence that stops abruptly
Intelligence that is never used
What are the trends shaping the future of BI/BA?
Improving data accessibility, analysis capabilities, and decision-making processes across industries
Limiting data analysis capabilities
Decreasing data accessibility
Ignoring data-driven decisions
What advantage do organizations gain by embracing BI/BA technologies and methodologies?
They become less efficient
They become irrelevant
They gain a competitive advantage in the increasingly data-driven business landscape
They lose competitiveness
