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Confluent CCDAK Exam Syllabus Topics:

Section	Weight	Objectives
Kafka Connect	15%	- Connect architecture: workers, connectors, tasks, converters - Source and sink connectors configuration - Data transformation and integration patterns
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
Application Testing	8%	- Mocking and test utilities - Unit testing producers and consumers - Integration testing and test clusters
Apache Kafka Fundamentals	23%	- Delivery semantics: at-most-once, at-least-once, exactly-once - Kafka architecture and data flow - Core concepts: brokers, topics, partitions, replicas, offsets - Configuration basics and cluster setup
Apache Kafka Streams	12%	- Windowing and time semantics - Error handling and state management - Streams API concepts: topology, processors, state stores - Stream processing operations: map, filter, join, aggregation
Application Observability	13%	- Tracing and performance tuning - Error diagnosis and troubleshooting - Logging and monitoring metrics

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Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions (Q22-Q27):

NEW QUESTION # 22
Your application is consuming from a topic configured with a deserializer.
It needs to be resilient to badly formatted records ("poison pills"). You surround the poll() call with a try/catch for RecordDeserializationException.
You need to log the bad record, skip it, and continue processing.
Which action should you take in the catch block?

A. Log the bad record and seek the consumer to the offset of the next record.
B. Throw a runtime exception to trigger a restart of the application.
C. Log the bad record, no other action needed.
D. Log the bad record and call the consumer.skip() method.

Answer: A

Explanation:
To skip a corrupted record and avoid failing the application, you must seek past the failed offset manually using consumer.seek(). This allows the application to resume consumption from the next offset.
From Kafka Consumer Error Handling Docs:
"On deserialization failure, you can catch RecordDeserializationException, log the error, and call seek() to the next offset to skip the bad record." A does not prevent re-processing the bad record.
C is invalid; there's no skip() method in the Kafka consumer API.
D results in service interruption - not ideal for resiliency.
Reference: Kafka Consumer API - Exception Handling and seek()

NEW QUESTION # 23
You are experiencing low throughput from a Java producer. You monitor the Kafka metrics and notice that the metrics for that producer show low I/O thread ratio and low I/O thread wait ratio.
What is most likely the cause of the slow producer performance?

A. Compression is enabled.
B. There is a bad data link layer (layer 2) connection from the producer to the cluster.
C. The producer is sending large batches of messages.
D. The producer code has an expensive callback function.

Answer: D

NEW QUESTION # 24
Which of the following Kafka Streams operators are stateful? (select all that apply)

A. reduce
B. joining
C. aggregate
D. flatmap
E. peek
F. count

Answer: A,B,C,F

Explanation:
Seehttps://kafka.apache.org/20/documentation/streams/developer-guide/dsl-api.html#stateful-transformations

NEW QUESTION # 25
Which of these joins does not require input topics to be sharing the same number of partitions?

A. KStream-GlobalKTable
B. KTable-KTable join
C. KStream-KTable join
D. KStream-KStream join

Answer: A

Explanation:
GlobalKTables have their datasets replicated on each Kafka Streams instance and therefore no repartitioning is required

NEW QUESTION # 26
(You create an Orders topic with 10 partitions.
The topic receives data at high velocity.
Your Kafka Streams application initially runs on a server with four CPU threads.
You move the application to another server with 10 CPU threads to improve performance.
What does this example describe?)

A. Plain Scaling
B. Horizontal Scaling
C. Vertical Scaling
D. Scaling Out

Answer: C

Explanation:
This scenario describes vertical scaling, as defined in the Apache Kafka Streams documentation and general distributed systems terminology. Vertical scaling refers to adding more resources (CPU, memory, or disk) to a single machine to increase processing capacity.
In this example, the Kafka Streams application is moved from a server with four CPU threads to one with ten CPU threads, increasing the available compute resources without increasing the number of application instances. Kafka Streams can take advantage of additional CPU threads through its num.stream.threads configuration, allowing more partitions to be processed in parallel within the same application instance.
Horizontal scaling (also known as scaling out) would involve adding more application instances across multiple servers, not upgrading a single server. Option D is therefore incorrect, and Option C is not a recognized scaling term.
Thus, this example clearly illustrates vertical scaling, where performance is improved by increasing the capacity of a single node.

NEW QUESTION # 27
......

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