CCDAK Exam Dumps - Confluent Certified Developer for Apache Kafka Certification Examination

When using Kafka Connect with sink connectors (like S3 Sink), each task is assigned one or more topic partitions. The total number of partitions (6) is evenly distributed across the available tasks (2). Thus, each task will handle 3 partitions.

From Kafka Connect Documentation:

“Kafka Connect divides the topic partitions among available tasks. For example, a topic with six partitions and two tasks results in each task handling three partitions.”

According to the official Apache Kafka documentation, log compaction is the mechanism used to retain only the latest record for each key in a topic. By setting cleanup.policy=compact, Kafka ensures that older records with the same key are eventually removed, leaving only the most recent value for each key.

This behavior is exactly suited for use cases such as tracking the current state of an entity, including GPS tracker locations, user profiles, or configuration data. In this scenario, the key (trackerId) uniquely identifies a tracker, and the value represents its most recent latitude and longitude.

Option B (retention.ms=infinite) retains all historical data, which contradicts the requirement. Option C (min.cleanable.dirty.ratio) controls when compaction runs, not what data is retained. Option D (retention.ms=0) would immediately delete all data and is unsafe.

Therefore, setting cleanup.policy=compact is the correct and officially documented solution to always retain the latest location per tracker.

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If the consumer fails to send heartbeats in time (due to being slow under load), it is considered dead, causing a rebalance. Increasing session.timeout.ms gives the consumer more time to process messages and send heartbeats, preventing premature removal.

From Kafka Docs > Heartbeats and Failures:

“If the consumer does not send a heartbeat in session.timeout.ms, it is considered dead and the group coordinator will trigger a rebalance.”

Reducing max.poll.records might slow consumption further. More consumers won’t help since each partition already has a dedicated consumer.

For Kafka Streams to perform joins, topics must be co-partitioned, meaning:

They have the same number of partitions (A), and

They use the same partitioning strategy (typically by key) (B)

From Kafka Streams Documentation > Joins:

“Co-partitioning is required for Kafka Streams joins. Topics must have the same number of partitions and use the same key-based partitioner.”

Value schema (C) and retention time (D) have no bearing on join compatibility.

Kafka Connect can run in standalone mode or distributed mode. For simple tasks like reading logs from a file on a single host, standalone mode is recommended.

From Kafka Connect User Guide:

“Standalone mode is useful when running connectors on a single machine (e.g., for development or simple deployments like log collection from a local file).”

Distributed mode is preferred for scalability and fault tolerance but overkill for this use case.

Unit Testing → MockProducer

Integration Testing → Testcontainers

Performance Testing → Trogdor

Mock Data Generation → Connect Datagen

MockProducer: Simulates a Kafka producer in unit tests (no real broker interaction).

Testcontainers: Spawns Kafka in Docker for real environment testing.

Trogdor: Kafka’s built-in performance load testing framework.

Connect Datagen: Creates sample source records for test and demo purposes.

From Kafka Developer Tools Guide:

“Kafka developers commonly use MockProducer for unit tests, Testcontainers for integration, and Trogdor for performance tests.”

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.

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According to the official Apache Kafka producer metrics documentation, the I/O thread ratio and I/O thread wait ratio indicate how busy the producer’s network I/O thread is and how much time it spends waiting. When both metrics are low, it means the I/O thread is underutilized and not blocked on network operations.

This strongly suggests that the bottleneck is not network-related. If there were network latency or a bad data link (Option B), the I/O thread wait ratio would be high. Similarly, large batches (Option A) or compression (Option D) generally increase throughput and would result in higher I/O utilization.

An expensive or blocking callback function (for example, heavy logic in onCompletion()) executes in the producer’s sender thread context. Kafka documentation explicitly warns that slow callbacks can throttle producer throughput because they delay the processing of acknowledgements and subsequent sends.

Therefore, the most likely and documented cause of low producer throughput with low I/O utilization is expensive callback logic in the producer code.

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