What is the primary purpose of Kafka Connect within the Kafka ecosystem?
Simplifying the integration of data between Kafka and external systems.
Managing and monitoring the health of Kafka clusters.
Providing a scalable and distributed messaging platform.
Real-time stream processing of data within Kafka topics.
In Kafka Streams, what is the primary difference between stateful and 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.
Stateful processing is more scalable than stateless processing.
Stateful processing allows access to historical data, while stateless processing only considers the current record.
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.
What happens to a consumer's offset when it encounters an error while processing a message?
The offset is automatically reset to the beginning of the partition.
The message is discarded and the offset is advanced.
The consumer is removed from the consumer group.
The offset is not updated until the message is successfully processed.
Which scenario would benefit from using a synchronous Kafka producer?
High-volume sensor data ingestion where throughput is a primary concern.
Logging system where message loss is acceptable.
Real-time data streaming where latency is critical.
Financial transaction processing where guaranteed message delivery is paramount.
How does Kafka Streams achieve fault tolerance?
By replicating stream processing tasks across multiple nodes.
By using a single, centralized processing unit.
By relying solely on message acknowledgments from consumers.
By storing all processed data in a separate, redundant database.
How are Kafka Connect connectors typically run in a production environment?
As standalone Java processes.
Within the Kafka broker processes.
As Docker containers orchestrated by Kubernetes.
Both A and C are common deployment methods.
Which mechanism is fundamental to Kafka's zero-copy technology for transferring data between the broker and the operating system?
Message compression
Data deduplication
Data encryption
Direct memory access (DMA)
Which of the following is NOT a core concept in stream processing?
Joining streams
Windowing
Aggregation
Batch processing
Which partitioning strategy in Kafka is most suitable when you need messages with the same key to be processed by the same consumer instance?
Time-based Partitioning
Random Partitioning
Key-based Partitioning
Round Robin Partitioning