What happens to the data on a broker that is permanently removed from a Kafka cluster without proper decommissioning?
It becomes inaccessible until the broker is added back.
It is automatically replicated to other brokers.
It is migrated to the ZooKeeper ensemble.
It is permanently lost.
Which Kafka Streams feature allows for joining data from multiple topics based on a common key?
Stream-Table Join
Windowed Aggregation
MapReduce
Branching
How are Kafka Connect connectors typically run in a production environment?
As Docker containers orchestrated by Kubernetes.
As standalone Java processes.
Both A and C are common deployment methods.
Within the Kafka broker processes.
What is the role of a 'state store' in Kafka Streams?
To cache data from external databases.
To store intermediate results of stateless operations.
To persist data required for stateful operations.
To buffer incoming records before processing.
Which scenario would benefit from using a synchronous Kafka producer?
Logging system where message loss is acceptable.
Financial transaction processing where guaranteed message delivery is paramount.
High-volume sensor data ingestion where throughput is a primary concern.
Real-time data streaming where latency is critical.
What is the primary method used by a Kafka Producer to send messages to a Kafka topic?
push()
transmit()
deliver()
send()
Which of the following is the function of a 'Sink Connector' in Kafka Connect?
It filters messages in a Kafka topic based on predefined criteria.
It replicates data between different Kafka clusters.
It retrieves data from a Kafka topic and writes it to an external system.
It aggregates data from multiple Kafka topics into a single topic.
In Kafka, which configuration setting controls the duration for which message acknowledgments from consumers are tracked?
offsets.retention.minutes
message.timeout.ms
replica.lag.time.max.ms
group.max.session.timeout.ms
What is the primary benefit of using Kafka's idempotent producer feature?
Increased throughput by reducing the need for message acknowledgments.
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.
How does Kafka Streams achieve fault tolerance?
By relying solely on message acknowledgments from consumers.
By using a single, centralized processing unit.
By replicating stream processing tasks across multiple nodes.
By storing all processed data in a separate, redundant database.