1. What is distributed data mining (DDM)?
A. Mining data on a single machine only
B. Mining data that is distributed across multiple machines or nodes
C. Storing data across multiple databases
D. Sharing data between different organizations
Answer: B
(Distributed Data Mining (DDM) refers to the process of mining data that is distributed across multiple machines or nodes.)
2. Which of the following is a key advantage of distributed data mining?
A. It requires less computational power
B. It can process large volumes of data spread across multiple machines
C. It simplifies data integration from different sources
D. It eliminates the need for data pre-processing
Answer: B
(Distributed data mining allows the processing of large volumes of data that is spread across multiple machines, improving scalability and efficiency.)
3. Which of the following is a common challenge in distributed data mining?
A. Data sparsity
B. Data communication and synchronization across nodes
C. Limited storage capacity
D. Lack of machine learning algorithms
Answer: B
(Data communication and synchronization across multiple nodes in a distributed environment can be challenging, especially when dealing with large datasets.)
4. In distributed data mining, what does data partitioning refer to?
A. Storing the data on a single machine
B. Splitting the dataset into smaller, more manageable parts that can be processed on different nodes
C. Combining data from multiple sources into one large dataset
D. Cleaning and normalizing the data
Answer: B
(Data partitioning involves dividing the dataset into smaller parts that can be processed in parallel on different nodes or machines.)
5. Which of the following is a type of distributed data mining architecture?
A. Centralized architecture
B. Grid computing architecture
C. Client-server architecture
D. Federated architecture
Answer: D
(Federated architecture is commonly used in distributed data mining, where multiple independent systems collaborate without central control.)
6. In distributed data mining, what is model aggregation?
A. The process of combining data from different nodes
B. The process of combining multiple models generated at different nodes into a single, unified model
C. The process of splitting large datasets into smaller sub-datasets
D. The process of cleaning data for analysis
Answer: B
(Model aggregation refers to the combination of models generated from different nodes in a distributed environment to create a unified model.)
7. Which of the following is a limitation of distributed data mining?
A. Inability to use machine learning algorithms
B. Increased network communication overhead
C. Decreased storage efficiency
D. Decreased processing power
Answer: B
(Distributed data mining often involves significant network communication overhead, especially when synchronizing data across nodes and transferring large volumes of data.)
8. Which of the following techniques is used in distributed data mining for ensuring privacy and security?
A. Data encryption and secure communication protocols
B. Parallel processing
C. Data compression
D. Data visualization
Answer: A
(Ensuring privacy and security in distributed data mining often involves techniques such as data encryption and secure communication protocols to protect data across nodes.)
9. What is horizontal data partitioning in distributed data mining?
A. Dividing data based on features or attributes
B. Dividing data into equal-sized chunks based on records or rows
C. Storing the data across multiple locations
D. Generating multiple models for each partition
Answer: B
(Horizontal data partitioning involves splitting the dataset by rows, where each partition contains a subset of the records or data entries.)
10. In distributed data mining, what does local data mining refer to?
A. Mining data that is available on a central server
B. Mining data stored in one physical location or node
C. Mining data across multiple nodes at once
D. Mining data using algorithms that require central control
Answer: B
(Local data mining refers to mining data stored in one physical location or node, as opposed to processing data across multiple nodes in a distributed environment.)