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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Application Observability | 13% | - Logging and monitoring metrics - Error diagnosis and troubleshooting - Tracing and performance tuning |
| Topic 2: Apache Kafka Fundamentals | 23% | - Kafka architecture and data flow - Core concepts: brokers, topics, partitions, replicas, offsets - Delivery semantics: at-most-once, at-least-once, exactly-once - Configuration basics and cluster setup |
| Topic 3: Kafka Connect | 15% | - Connect architecture: workers, connectors, tasks, converters - Data transformation and integration patterns - Source and sink connectors configuration |
| Topic 4: Apache Kafka Streams | 12% | - Stream processing operations: map, filter, join, aggregation - Streams API concepts: topology, processors, state stores - Error handling and state management - Windowing and time semantics |
| Topic 5: Application Testing | 8% | - Mocking and test utilities - Integration testing and test clusters - Unit testing producers and consumers |
| Topic 6: Apache Kafka Application Development | 28% | - Schema management: Confluent Schema Registry, compatibility rules, evolution - Transactional messaging and idempotence - Producer API: configuration, serialization, partitioning, error handling - Consumer API: configuration, offset management, consumer groups, rebalancing |
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NEW QUESTION # 17
A client connects to a broker in the cluster and sends a fetch request for a partition in a topic. It gets an exception Not Leader For Partition Exception in the response. How does client handle this situation?
Answer: B
Explanation:
In case the consumer has the wrong leader of a partition, it will issue a metadata request. The Metadata request can be handled by any node, so clients know afterwards which broker are the designated leader for the topic partitions. Produce and consume requests can only be sent to the node hosting partition leader.
NEW QUESTION # 18
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: C
Explanation:
Increasing tasks.max allows the connector to create multiple tasks, each responsible for processing different tables or table partitions. If only one task is used, adding more workers has no effect.
From Kafka 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 # 19
Which configuration is used to determine which directory the connectors are stored in?
Answer: B
NEW QUESTION # 20
There are 3 producers writing to a topic with 5 partitions. There are 5 consumers consuming from the topic.
How many Controllers will be present in the cluster?
Answer: D
Explanation:
There is only one controller in a cluster at all times.
NEW QUESTION # 21
You have a Kafka client application that has real-time processing requirements.
Which Kafka metric should you monitor?
Answer: D
Explanation:
For real-time applications, the key metric to monitor is consumer lag - the difference between the latest offset on the broker and the last committed offset by the consumer.
From Kafka Monitoring Guide:
"Consumer lag is the most important metric for real-time applications. It tells you how far behind the consumer is from the latest data in Kafka." B relates to replication, not client responsiveness.
C tracks group membership stability, not data delay.
D tracks input throughput, not processing latency.
Reference: Kafka Operations > Monitoring > Consumer Lag
NEW QUESTION # 22
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