What is the significance of the 'unclean.leader.election.enable' configuration parameter during broker failures?
It allows for faster leader election but might lead to data loss.
It ensures no data loss during leader election but might increase unavailability.
It controls the replication factor for partitions.
It defines the time a broker is considered dead before triggering a leader election.
What is the purpose of using tumbling windows in Kafka Streams?
To divide data into sessions based on user activity.
To overlap windows for smoothing out aggregated results.
To trigger aggregations only when a specific event occurs.
To process records in fixed-size, non-overlapping time intervals.
How does increasing the replication factor of a topic affect the availability and durability of data in Kafka?
Higher replication factor increases both availability and durability without any drawbacks.
Higher replication factor has no impact on availability or durability.
Higher replication factor increases durability but may slightly reduce write availability.
Higher replication factor increases availability but reduces durability.
Which of the following is NOT a common category of Kafka metrics?
Producer metrics
Topic metrics
Consumer metrics
Authentication metrics
Which partitioning strategy in Kafka is most suitable when you need messages with the same key to be processed by the same consumer instance?
Round Robin Partitioning
Random Partitioning
Key-based Partitioning
Time-based Partitioning
What is the primary method used by a Kafka Producer to send messages to a Kafka topic?
send()
transmit()
deliver()
push()
What is the purpose of stateful operations in Kafka Streams?
To maintain and update information across multiple messages in a stream.
To filter and route messages based on content.
To store processed data permanently in a relational database.
To ensure exactly-once message delivery semantics.
What is a key advantage of using KSQL for stream processing in Kafka?
It allows for direct manipulation of Kafka's underlying storage files.
It provides a simpler, SQL-based abstraction for building stream processing applications.
It offers significantly better performance compared to using Kafka Streams API.
It eliminates the need for any programming, relying solely on SQL commands.
What happens to a consumer's offset when it encounters an error while processing a message?
The consumer is removed from the consumer group.
The message is discarded and the offset is advanced.
The offset is not updated until the message is successfully processed.
The offset is automatically reset to the beginning of the partition.
What type of metrics would you monitor to track the rate at which messages are being produced to a Kafka topic?
Replication lag metrics
Consumer lag metrics
Broker disk usage metrics
Producer request rate metrics