MCQs on Cassandra Use Cases and Real-World Applications | Cassandra
Chapter 10 explores Cassandra’s versatility across various real-world applications, including social media platforms, IoT, banking, e-commerce, and design challenges. Understanding how Cassandra fits into these domains will enhance your ability to optimize its capabilities. These 30 MCQs will help you grasp its use cases and challenges in practical scenarios.
Social Media and Messaging Platforms
- What is a key advantage of using Cassandra for social media platforms?
a) High consistency for real-time data
b) Horizontal scalability for handling large traffic
c) Complex query support
d) ACID transactions - In social media applications, how does Cassandra handle large volumes of data?
a) By using multiple partitions for data distribution
b) By indexing every post for fast retrieval
c) By employing complex JOIN operations
d) By using traditional relational databases - What type of data is typically stored in Cassandra for messaging applications?
a) Structured data with complex relationships
b) Unstructured and semi-structured data
c) Time-sensitive transactional data
d) Historical data with frequent updates - Which feature of Cassandra is beneficial for real-time social media updates?
a) Low latency write operations
b) ACID transaction support
c) Advanced data normalization
d) Complex data aggregation - Why is Cassandra preferred for messaging systems with high traffic?
a) It uses a master-slave architecture
b) It provides linear scalability
c) It supports SQL-like queries
d) It minimizes storage overhead - In social media use cases, what is typically used as the partition key in Cassandra?
a) User ID or session ID
b) Message timestamp
c) Post content
d) User location - Which design pattern in Cassandra helps scale social media platforms effectively?
a) Vertical scaling
b) Master-slave architecture
c) Horizontal scaling
d) Data replication - How does Cassandra ensure high availability in messaging platforms?
a) Through master-slave replication
b) By storing data in one centralized server
c) Using peer-to-peer distributed architecture
d) By clustering data in one node
IoT and Time-Series Data
- Why is Cassandra ideal for IoT applications?
a) It supports complex relationships
b) It handles massive amounts of time-series data efficiently
c) It ensures data consistency across nodes
d) It uses ACID transactions for high precision - Which of the following is a common use case for Cassandra in IoT?
a) Real-time analytics on temperature sensor data
b) Running SQL queries on historical data
c) Storing transactional financial records
d) Performing complex joins across devices - In IoT applications, Cassandra handles time-series data by:
a) Storing data in fixed schema tables
b) Using timestamp-based partitioning
c) Storing data in JSON format
d) Normalizing data into multiple tables - How does Cassandra ensure scalability in IoT applications?
a) By increasing CPU power
b) By using sharding and horizontal scaling
c) Through complex queries
d) By reducing storage requirements - In time-series data storage, what Cassandra feature allows efficient retrieval of recent data?
a) Composite primary keys
b) Clustering columns
c) Secondary indexes
d) Data denormalization - What kind of data model is best suited for IoT sensor data in Cassandra?
a) Relational model
b) Key-value store
c) Wide-column store
d) Graph database - How does Cassandra handle the continuous ingestion of time-series data from IoT devices?
a) By compressing the data before storage
b) Through real-time data streaming and continuous writes
c) By using batch processing for data ingestion
d) Through complex data transformation pipelines - What is the main challenge in using Cassandra for time-series data in IoT?
a) Lack of real-time data ingestion support
b) Handling large volumes of data with low latency
c) The complexity of schema design
d) Lack of support for time-based queries
Banking and Fraud Detection
- What is the role of Cassandra in banking applications?
a) Storing complex financial transactions in relational tables
b) Providing high availability and scalability for transactional data
c) Running complex SQL queries for financial analytics
d) Supporting multi-dimensional data modeling - How does Cassandra support fraud detection in banking?
a) By ensuring transactional consistency across databases
b) Through real-time processing of financial transactions
c) By using advanced AI models for fraud prediction
d) By integrating with other banking systems for centralized data - Why is Cassandra used for fraud detection in high-volume environments?
a) It supports complex analytics over large datasets
b) It ensures high availability and can scale horizontally
c) It uses a traditional relational database model
d) It stores only small datasets efficiently - What kind of data does Cassandra typically handle in fraud detection systems?
a) Transactional logs with high write throughput
b) Small sets of static financial data
c) Real-time monitoring of fraud activities
d) Historical account balances - In Cassandra-based banking applications, what helps achieve low-latency transaction processing?
a) ACID compliance
b) Peer-to-peer architecture
c) Data consistency models
d) High availability and horizontal scaling - How does Cassandra provide scalability for banking applications dealing with large transaction volumes?
a) By using replication and partitioning
b) By reducing data redundancy
c) Through centralized server management
d) By increasing data normalization - Which Cassandra feature is crucial for handling high-frequency financial transaction data?
a) Write-heavy workloads with linear scalability
b) Relational schema
c) Data consistency with strict transactions
d) Complex aggregation functions - What is a challenge in using Cassandra for fraud detection systems?
a) Complex data transformations
b) Inability to scale horizontally
c) Maintaining real-time consistency for fraud alerts
d) Managing high-frequency writes
E-Commerce and Personalization
- Why is Cassandra used in e-commerce applications?
a) It provides support for complex SQL queries
b) It offers real-time product recommendation engines
c) It supports large-scale transactions and inventory data
d) It handles structured data in tabular format - How does Cassandra help in personalizing user experiences on e-commerce platforms?
a) By enabling real-time data processing and recommendation engines
b) By analyzing large sets of relational data
c) By using multi-dimensional data models for product searches
d) By maintaining historical transactional data - Which feature of Cassandra is most beneficial for personalized product recommendations?
a) Linear scalability for handling millions of requests
b) Complex relational joins
c) Real-time query processing on large datasets
d) Full-text search indexing - How does Cassandra handle product inventory data in e-commerce systems?
a) By using relational databases for inventory tracking
b) Through real-time updates and distributed data storage
c) By using SQL queries for quick inventory lookups
d) By storing data in a centralized database - What is a primary use of Cassandra in e-commerce platforms?
a) Storing user account information with strong ACID properties
b) Managing product catalog data for high traffic loads
c) Running large-scale business intelligence reports
d) Integrating with external payment gateways - How does Cassandra support high availability in e-commerce applications?
a) Through a master-slave replication model
b) By offering cross-data-center replication and decentralized architecture
c) By using traditional SQL databases for inventory management
d) Through single-node deployment models
Answer Key
| Qno | Answer |
|---|---|
| 1 | b) Horizontal scalability for handling large traffic |
| 2 | a) By using multiple partitions for data distribution |
| 3 | b) Unstructured and semi-structured data |
| 4 | a) Low latency write operations |
| 5 | b) It provides linear scalability |
| 6 | a) User ID or session ID |
| 7 | c) Horizontal scaling |
| 8 | c) Using peer-to-peer distributed architecture |
| 9 | b) It handles massive amounts of time-series data efficiently |
| 10 | a) Real-time analytics on temperature sensor data |
| 11 | b) Using timestamp-based partitioning |
| 12 | b) By using sharding and horizontal scaling |
| 13 | b) Clustering columns |
| 14 | b) Through real-time data streaming and continuous writes |
| 15 | b) Handling large volumes of data with low latency |
| 16 | b) Handling large volumes of data with low latency |
| 17 | b) Providing high availability and scalability for transactional data |
| 18 | b) Through real-time processing of financial transactions |
| 19 | b) It ensures high availability and can scale horizontally |
| 20 | a) Transactional logs with high write throughput |
| 21 | d) High availability and horizontal scaling |
| 22 | a) By using replication and partitioning |
| 23 | a) Write-heavy workloads with linear scalability |
| 24 | c) Maintaining real-time consistency for fraud alerts |
| 25 | c) It supports large-scale transactions and inventory data |
| 26 | a) By enabling real-time data processing and recommendation engines |
| 27 | c) Real-time query processing on large datasets |
| 28 | b) Through real-time updates and distributed data storage |
| 29 | b) Managing product catalog data for high traffic loads |
| 30 | b) By offering cross-data-center replication and decentralized architecture |