In Kafka Streams, what is the primary difference between stateful and stateless processing?
Stateful processing allows access to historical data, while stateless processing only considers the current record.
Stateful processing is more scalable than stateless processing.
Stateful processing is used for filtering data, while stateless processing is used for transformations.
Stateless processing is faster than stateful processing because it does not require data storage.
What is the primary benefit of using Kafka's idempotent producer feature?
Improved message ordering guarantees within a partition.
Automatic data balancing across multiple Kafka brokers.
Elimination of duplicate messages on the broker due to producer retries.
Increased throughput by reducing the need for message acknowledgments.
How does increasing the replication factor of a topic affect the availability and durability of data in Kafka?
Higher replication factor has no impact on availability or durability.
Higher replication factor increases both availability and durability without any drawbacks.
Higher replication factor increases durability but may slightly reduce write availability.
Higher replication factor increases availability but reduces durability.
How are Kafka Connect connectors typically run in a production environment?
Within the Kafka broker processes.
Both A and C are common deployment methods.
As standalone Java processes.
As Docker containers orchestrated by Kubernetes.
In Kafka's architecture, which component is responsible for storing and replicating message data?
Producer
Consumer
ZooKeeper
Broker
Which method in the Kafka Consumer API is used to retrieve a batch of records from a topic?
fetch()
consume()
poll()
receive()
Which component in Kafka is responsible for managing the state of tasks and ensuring fault tolerance within a Kafka Streams application?
Kafka Streams API
Kafka Producer
Kafka Connect
In Kafka Connect, what is the role of a 'Source Connector'?
It writes data from a Kafka topic to an external system.
It routes messages between different topics within a Kafka cluster.
It transforms data within a Kafka topic before sending it to a sink.
It consumes data from an external system and publishes it to a Kafka topic.
Which Kafka Streams feature allows for joining data from multiple topics based on a common key?
Branching
MapReduce
Windowed Aggregation
Stream-Table Join
What is the primary purpose of log compaction in Kafka?
Retaining the latest value for each key
Deleting old messages based on time
Improving message compression
Optimizing message routing