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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Apache Kafka Application Development | 28% | - Transactional messaging and idempotence - Schema management: Confluent Schema Registry, compatibility rules, evolution - Producer API: configuration, serialization, partitioning, error handling - Consumer API: configuration, offset management, consumer groups, rebalancing |
| Topic 2: Kafka Connect | 15% | - Source and sink connectors configuration - Data transformation and integration patterns - Connect architecture: workers, connectors, tasks, converters |
| Topic 3: Application Testing | 8% | - Mocking and test utilities - Unit testing producers and consumers - Integration testing and test clusters |
| Topic 4: Application Observability | 13% | - Tracing and performance tuning - Logging and monitoring metrics - Error diagnosis and troubleshooting |
| Topic 5: Apache Kafka Streams | 12% | - Streams API concepts: topology, processors, state stores - Error handling and state management - Windowing and time semantics - Stream processing operations: map, filter, join, aggregation |
| Topic 6: Apache Kafka Fundamentals | 23% | - Kafka architecture and data flow - Core concepts: brokers, topics, partitions, replicas, offsets - Configuration basics and cluster setup - Delivery semantics: at-most-once, at-least-once, exactly-once |
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NEW QUESTION # 15
Which of the following is true regarding thread safety in the Java Kafka Clients?
Answer: C,D
Explanation:
KafkaConsumer is not thread-safe, KafkaProducer is thread safe.
NEW QUESTION # 16
You use Kafka Connect with the JDBC source connector to extract data from a large database and push it into Kafka.
The database contains tens of tables, and the current connector is unable to process the data fast enough.
You add more Kafka Connect workers, but throughput doesn't improve.
What should you do next?
Answer: D
Explanation:
Increasingtasks.maxallows the connector to createmultiple tasks, each responsible for processing different tables or table partitions. If onlyone taskis used,adding more workers has no effect.
FromKafka Connect JDBC Connector Docs:
"Use tasks.max to configure how many tasks are created for the connector. Each task can process a subset of the data."
* A and C help only if the bottleneck is in Kafka, not Connect.
* D is a large architectural change, not the first step.
Reference:Kafka Connect JDBC Connector Configuration
NEW QUESTION # 17
If I want to have an extremely high confidence that leaders and replicas have my data, I should use
Answer: A
Explanation:
acks=all means the leader will wait for all in-sync replicas to acknowledge the record. Also the min in-sync replica setting specifies the minimum number of replicas that need to be in-sync for the partition to remain available for writes.
NEW QUESTION # 18
A Zookeeper ensemble contains 5 servers. What is the maximum number of servers that can go missing and the ensemble still run?
Answer: D
Explanation:
majority consists of 3 zk nodes for 5 nodes zk cluster, so 2 can fail
NEW QUESTION # 19
There are five brokers in a cluster, a topic with 10 partitions and replication factor of 3, and a quota of producer_bytes_rate of 1 MB/sec has been specified for the client. What is the maximum throughput allowed for the client?
Answer: D
Explanation:
Each producer is allowed to produce @ 1MB/s to a broker. Max throughput 5 * 1MB, because we have 5 brokers.
NEW QUESTION # 20
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