Cluster Point Quiz

What is the primary function of a node in Clusterpoint's database architecture?

• Data visualization

• Data storage and retrieval

• User authentication

• Network monitoring

How does Clusterpoint ensure fault tolerance and high availability?

• By limiting the number of nodes in the cluster

• By relying on a single centralized server

• By replicating data across multiple nodes

• By disabling data replication

Which of the following best describes the data model used in Clusterpoint database?

• Relational model

• Document-oriented model

• Graph model

• Key-value model

What is one of the key features of Clusterpoint database that makes it suitable for handling large volumes of data efficiently?

• Relational querying

• XML-based indexing

• Real-time data processing

• Static schema enforcement

Which feature of Clusterpoint enables it to accommodate growing data volumes and user demands ?

• Scalability

• Fault Tolerance

• Real-time Data Processing

• High Availability

Which feature of Clusterpoint ensures continuous access to data even in the event of node failures or network disruptions?

• Flexible Data Model

• Multi-Model Support

• Security and Compliance

• Fault Tolerance

What is a cluster point database primarily designed for?

• Storing unstructured data

• Analyzing structured data

• Handling real-time data processing

• Managing geographical data

Which of the following is a key characteristic of cluster point databases?

• High consistency and low availability

• Low consistency and high availability

• Strong schema enforcement

• Limited scalability options

What contributed to the failure of Clusterpoint DB as a NoSQL document database management system?

• Ineffective marketing strategies

• Lack of fault tolerance features

• Overwhelming scalability capabilities

• Excessive investment in security measures

In which scenario would a real-time application benefit from using Clusterpoint DB?

• Storing static, unchanging data for archival purposes

• Analyzing historical trends in large datasets

• Processing and responding to streaming sensor data in real-time

• Managing occasional, low-volume data transactions