Cassandra Data Replication and Consistency Levels Quiz Quiz

Assess your understanding of Cassandra data replication strategies, consistency levels, and related concepts. This quiz covers replication factors, consistency mechanisms, quorum reads and writes, and how these settings impact reliability and performance in distributed database systems.

1. Replication Factor Purpose

   What is the main purpose of setting a replication factor for a keyspace in Cassandra?

   1. To define how often data is compacted
   2. To determine how many copies of data are stored across nodes
   3. To limit the number of queries per second allowed
   4. To increase the size of data partitions

   Explanation: The replication factor specifies how many nodes will contain copies of each piece of data, enhancing fault tolerance and availability. Increasing partition size, limiting queries, and setting compaction frequency are unrelated; those are managed by different settings and processes.

2. Consistency Level 'ONE'

   When using the consistency level 'ONE' for a read operation in Cassandra, what is required for the operation to succeed?

   1. A majority of replicas must reply
   2. At least one replica returns the requested data
   3. The client must send a request to every node in the cluster
   4. All replicas containing the data must respond

   Explanation: With the 'ONE' consistency level, only a single replica needs to respond with the data, making it faster but less safe than requiring more nodes. 'ALL' would require all replicas, 'QUORUM' needs a majority, and clients never need to contact every node directly.

3. Quorum Consistency

   What does the 'QUORUM' consistency level mean when executing a write operation in Cassandra with a replication factor of three?

   1. Only one replica node must confirm the write
   2. All three replica nodes must acknowledge the write
   3. At least two replica nodes must acknowledge the write
   4. Write is sent to any node regardless of replicas

   Explanation: 'QUORUM' represents a majority of replicas, so with a replication factor of three, two nodes must confirm the write. 'ALL' would need three, 'ONE' needs only one, and sending a write to any node regardless of replica status ignores how replication works.

4. Replication Strategy Choice

   Which replication strategy should you choose to distribute data evenly across data centers in a multi-location setup?

   1. SimpleStrategy
   2. SingleReplication
   3. LocalStrategy
   4. NetworkTopologyStrategy

   Explanation: NetworkTopologyStrategy is designed for distributing replicas across multiple data centers to improve reliability. SimpleStrategy is intended for single data center use, LocalStrategy is for internal system tables only, and SingleReplication is not a recognized option.

5. Read Repair Role

   What is the function of read repair in Cassandra?

   1. It updates out-of-date replicas during a read operation
   2. It increases the replication factor
   3. It compresses SSTable files periodically
   4. It backs up data to external storage

   Explanation: Read repair helps keep replicas synchronized by updating any that have stale data during a read. Backing up, compressing SSTables, and changing the replication factor are distinct processes unrelated to read repair.

6. Eventual Consistency

   What does 'eventual consistency' mean in distributed databases like Cassandra?

   1. All replicas will become consistent over time if no new updates are made
   2. Data changes are never synchronized between replicas
   3. All reads are instantly consistent across nodes
   4. Only one replica ever holds the latest copy

   Explanation: Eventual consistency allows some replicas to be temporarily out of sync, but if no new writes are made, all replicas will become consistent. Instant consistency across nodes is strong consistency, data being never synchronized is not accurate, and only one replica holding the latest copy is incorrect.

7. Impact of Increasing Replication Factor

   What is the effect of increasing the replication factor from two to three in Cassandra?

   1. It increases data availability and fault tolerance
   2. It disables automatic data repair
   3. It decreases the number of nodes required for a cluster
   4. It reduces the size of each data partition

   Explanation: A higher replication factor means more copies of data, improving availability and resilience against node failures. It does not reduce required nodes, alter partition sizes, or affect the automatic repair processes.

8. Consistency Level 'ALL'

   What must happen for a write at the 'ALL' consistency level to be considered successful in Cassandra?

   1. A single replica handles the write immediately
   2. Any two replicas must confirm the write
   3. Only the coordinator node must log the write
   4. All replicas for the partition must acknowledge the write

   Explanation: The 'ALL' consistency level requires a write to be confirmed by every replica, ensuring the strongest consistency but sacrificing availability in the event a replica is down. The coordinator alone logging the write is insufficient, a single replica is not enough, and 'any two' ignores the total replica count.

9. Write Consistency and Data Loss

   If a write is performed with the consistency level 'ONE' and the only responding node fails before replicating to others, what risk does this pose?

   1. The data can be lost if the responding node fails before replication
   2. The data is fully safe across all replicas
   3. All replicas immediately have identical data
   4. The write automatically upgrades to 'ALL'

   Explanation: Using 'ONE' means only one replica needs to acknowledge the write, so if it fails quickly, the data isn't safe yet. Full safety would need higher consistency, replicas are not always immediately identical with 'ONE', and consistency levels do not automatically upgrade.

10. Tunable Consistency Benefit

    Why is tunable consistency considered an advantage in Cassandra?

    1. It allows balancing between consistency and availability based on application needs
    2. It fixes corrupted data automatically with every read
    3. It ensures the strongest consistency at all times
    4. It increases query speed by default

    Explanation: Tunable consistency lets users choose the right trade-off for their application, prioritizing speed or reliability as needed. Always enforcing the strongest consistency eliminates this flexibility. Automatic repair and improved query speed are unrelated to tunable consistency itself.