What does JMX stand for in the context of Kafka monitoring?
Just Monitor XML
JSON Metric eXchange
Java Management eXtensions
Java Message eXport
How does Grafana complement Prometheus in Kafka monitoring?
It sends alerts based on Kafka metrics collected by JMX
It acts as a JMX client to collect metrics from Kafka brokers
It replaces Prometheus as the primary metric storage
It provides customizable dashboards and visualizations for Kafka metrics stored in Prometheus
How does Kafka ensure message ordering within a partition?
By appending messages sequentially to the log
By using message keys for sorting
By assigning sequential timestamps to messages
By employing a priority queue mechanism
What is a key difference between Source Connectors and Sink Connectors in Kafka Connect?
Source Connectors handle real-time data, while Sink Connectors handle batch data.
Source Connectors push data, while Sink Connectors pull data.
Source Connectors are stateful, while Sink Connectors are stateless.
Source Connectors require custom coding, while Sink Connectors use pre-built configurations.
What information is typically included in a Kafka Connect connector configuration file?
The Kafka producer and consumer configurations for message production and consumption.
The source or sink system connection details, data format, and topic mapping.
All of the above.
The security credentials for accessing the Kafka Connect cluster and topics.
What happens to the data on a broker that is permanently removed from a Kafka cluster without proper decommissioning?
It is automatically replicated to other brokers.
It is permanently lost.
It becomes inaccessible until the broker is added back.
It is migrated to the ZooKeeper ensemble.
How does Kafka store messages within a topic partition on disk?
As individual files for each message
As an append-only log file
Using a B-tree index structure
In a hash table for quick retrieval
Which Kafka Streams feature allows for joining data from multiple topics based on a common key?
Branching
Stream-Table Join
MapReduce
Windowed Aggregation
How does increasing the replication factor of a topic affect the availability and durability of data in Kafka?
Higher replication factor increases durability but may slightly reduce write availability.
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 availability but reduces durability.
What is the purpose of consumer groups in Kafka?
To guarantee message ordering for all consumers.
To replicate messages across multiple data centers.
To allow multiple consumers to subscribe to the same topic and each process a subset of messages.
To store messages persistently on disk.