The Confluent Certified Developer for Apache Kafka (CCDAK) exam validates your ability to build, deploy, and maintain event streaming applications on Apache Kafka. This certification is designed for developers who work with Kafka in production environments and need to demonstrate practical expertise. This page provides a clear roadmap of the exam syllabus, question formats, and actionable preparation strategies to help you pass with confidence.
Use this topic map to guide your study for Confluent CCDAK (Certified Developer for Apache Kafka) within the Confluent Certified Developer path.
The CCDAK exam uses multiple question types to assess both conceptual knowledge and practical problem-solving ability. Questions progress in difficulty and reflect real-world scenarios you will encounter as a Kafka developer.
Effective preparation requires mapping the five exam domains to a structured study plan and practicing with realistic questions. Allocate time proportionally to each topic, prioritizing areas where you have less hands-on experience. Regular practice and review of explanations will reinforce weak areas and build confidence.
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Visit the exam page to download the PDF, Online Practice Test, or get a Bundle Discount offer for both formats: Certified Developer for Apache Kafka.
Apache Kafka Application Development and Apache Kafka Streams typically account for the largest portion of exam items because they test hands-on development skills. However, all five domains are important; a strong foundation in Fundamentals supports your ability to answer scenario-based questions correctly.
In practice, you start with Fundamentals knowledge to design your architecture, move to Application Development to build producers and consumers, use Kafka Streams for event processing logic, write tests to validate behavior, and finally deploy observability to monitor production. Understanding these connections helps you answer questions that span multiple topics.
Ideally, you should have built at least one complete Kafka application (producer, consumer, or Streams topology) in a development or test environment. This experience helps you understand configuration trade-offs and error scenarios. If you lack hands-on exposure, prioritize labs and code examples in your study plan.
Candidates often overlook configuration details (like acks, retries, or consumer group settings), misunderstand offset management, or confuse stateless versus stateful operations in Streams. Carefully read scenario questions to identify what the code is trying to achieve, and always consider edge cases like broker failures or network delays.
Review your practice test results to identify weak topics, then re-read explanations for those items rather than re-memorizing facts. Take one full-length timed practice test to build confidence and check your pacing. On the day before the exam, review key definitions and architecture diagrams, but avoid cramming new material.
There are 3 brokers in the cluster. You want to create a topic with a single partition that is resilient to one broker failure and one broker maintenance. What is the replication factor will you specify while creating the topic?
1 is not possible as it doesn't provide resilience to failure, 2 is not enough as if we take a broker down for maintenance, we cannot tolerate a broker failure, and 6 is impossible as we only have 3 brokers (RF cannot be greater than the number of brokers). Here the correct answer is 3
The exactly once guarantee in the Kafka Streams is for which flow of data?
Kafka Streams can only guarantee exactly once processing if you have a Kafka to Kafka topology.
A Zookeeper ensemble contains 5 servers. What is the maximum number of servers that can go missing and the ensemble still run?
majority consists of 3 zk nodes for 5 nodes zk cluster, so 2 can fail
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)
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
In Kafka, every broker... (select three)
Kafka topics are divided into partitions and spread across brokers. Each brokers knows about all the metadata and each broker is a bootstrap broker, but only one of them is elected controller