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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Apache Kafka Streams | 12% | - Error handling and state management - Streams API concepts: topology, processors, state stores - Windowing and time semantics - Stream processing operations: map, filter, join, aggregation |
| Topic 2: Application Testing | 8% | - Mocking and test utilities - Integration testing and test clusters - Unit testing producers and consumers |
| Topic 3: Kafka Connect | 15% | - Connect architecture: workers, connectors, tasks, converters - Source and sink connectors configuration - Data transformation and integration patterns |
| Topic 4: Application Observability | 13% | - Logging and monitoring metrics - Error diagnosis and troubleshooting - Tracing and performance tuning |
| Topic 5: Apache Kafka Fundamentals | 23% | - Delivery semantics: at-most-once, at-least-once, exactly-once - Core concepts: brokers, topics, partitions, replicas, offsets - Configuration basics and cluster setup - Kafka architecture and data flow |
| Topic 6: Apache Kafka Application Development | 28% | - Transactional messaging and idempotence - Producer API: configuration, serialization, partitioning, error handling - Consumer API: configuration, offset management, consumer groups, rebalancing - Schema management: Confluent Schema Registry, compatibility rules, evolution |
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NEW QUESTION # 16
When auto.create.topics.enable is set to true in Kafka configuration, what are the circumstances under which a Kafka broker automatically creates a topic? (select three)
Answer: A,B,C
Explanation:
A kafka broker automatically creates a topic under the following circumstances- When a producer starts writing messages to the topic - When a consumer starts reading messages from the topic - When any client requests metadata for the topic
NEW QUESTION # 17
You are running a Kafka Streams application in a Docker container managed by Kubernetes, and upon application restart, it takes a long time for the docker container to replicate the state and get back to processing the dat a. How can you improve dramatically the application restart?
Answer: A
Explanation:
Although any Kafka Streams application is stateless as the state is stored in Kafka, it can take a while and lots of resources to recover the state from Kafka. In order to speed up recovery, it is advised to store the Kafka Streams state on a persistent volume, so that only the missing part of the state needs to be recovered.
NEW QUESTION # 18
Where are source connector offsets stored?
Answer: D
Explanation:
Kafka Connectsource connectorsuse theoffset.storage.topicconfiguration parameter to define where to store offsets that track the connector's positionin the source system (e.g., database, file, etc.).
FromKafka Connect Worker Configuration:
"offset.storage.topic specifies the topic in Kafka where the connector stores offsets for each source partition." This allows Kafka Connect toresumefrom the correct position on restart.
* The other options (B-D) are not valid Kafka configs.
Reference:Kafka Connect Worker Configs > offset.storage.topic
NEW QUESTION # 19
(You are developing a Java application that includes a Kafka consumer.
You need to integrate Kafka client logs with your own application logs.
Your application is using the Log4j2 logging framework.
Which Java library dependency must you include in your project?)
Answer: D
Explanation:
According to the official Apache Kafka client documentation, Kafka uses SLF4J (Simple Logging Facade for Java) for all client-side logging. SLF4J is only an abstraction layer and requires a concrete logging implementation to route log messages to the actual logging framework used by the application.
Since the application uses Log4j2, an SLF4J-to-Log4j2 binding must be provided. The correct and officially recommended dependency is org.apache.logging.log4j:log4j-slf4j-impl. This dependency acts as the SLF4J implementation and forwards all Kafka client log messages to Log4j2, ensuring consistent logging across the application.
Option B is incorrect because it targets the legacy Log4j 1.2 framework, which is deprecated and incompatible with Log4j2. Option C is insufficient because Log4j2 alone does not satisfy Kafka's SLF4J dependency. Option D is incorrect because Kafka intentionally does not include a logging implementation transitively, allowing applications to choose their preferred logging framework.
Therefore, including the SLF4J implementation for Log4j2 is required to properly integrate Kafka client logs.
NEW QUESTION # 20
You are experiencing low throughput from a Java producer.
Metrics show low I/O thread ratio and low I/O thread wait ratio.
What is the most likely cause of the slow producer performance?
Answer: C
Explanation:
Low I/O thread activity with blocked throughput often indicates thatproducer callbacks are consuming too much time, causing the sender thread to block while waiting for onCompletion() to finish.
FromKafka Producer Performance Guide:
"Expensive logic in callbacks (e.g., I/O or complex computation) can block the sender thread, reducing throughput."
* Compression (A) may slightly impact CPU but not I/O thread usage.
* Large batches (B) improve throughput if managed correctly.
* A Layer 2 network issue (C) would lead to packet loss, not specifically low callback metrics.
Reference:Kafka Producer Metrics and Performance Tuning
NEW QUESTION # 21
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