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Confluent CCDAK Exam Questions
- Topic 1: Apache Kafka® Fundamentals: This section of the exam measures the skills of a Kafka Developer and covers the essential concepts required to work effectively with Kafka. It includes understanding how Kafka operates under the hood, including its architecture, brokers, topics, partitions, replication, producers, and consumers. A strong grasp of Kafka fundamentals is necessary to build scalable, fault-tolerant applications, and this section ensures the developer can configure and manage these components properly.
- Topic 2: Apache Kafka® Application Development: This section of the exam assesses a Data Engineer's ability to build applications using Kafka APIs. It focuses on the development of producer and consumer applications, the use of Kafka client libraries, and how to properly handle serialization and deserialization. Candidates are expected to know how to write code that interacts efficiently with Kafka, manage offset commits, and ensure message reliability and ordering within their applications.
- Topic 3: Apache Kafka® Streams: This section of the exam measures the skills of a Kafka Developer and focuses on using Kafka Streams to build real-time, event-driven applications. It includes the basics of stream processing, such as stateless and stateful transformations, windowing, joins, and interactive queries. Developers need to demonstrate the ability to use Kafka Streams to process and analyze data in motion with scalability and fault tolerance in mind.
- Topic 4: Kafka Connect: This part evaluates the Data Engineer's ability to use Kafka Connect for integrating external systems with Kafka. It covers both source and sink connectors, configuration of connectors, and the management of data pipelines between Kafka and various data systems like databases, cloud storage, or file systems. Engineers are expected to show an understanding of how Kafka Connect simplifies large-scale data ingestion and export tasks.
- Topic 5: Application Testing: This section of the exam tests a Kafka Developer’s knowledge of testing Kafka-based applications. It includes techniques for unit testing, integration testing, and mocking Kafka for development environments. Candidates should be familiar with best practices for ensuring application correctness and stability when interacting with Kafka systems.
- Topic 6: Application Observability: This section measures the skills of a Data Engineer in observing and maintaining Kafka-based applications. It focuses on the use of logging, metrics, and tracing to monitor system behavior and performance. Engineers are expected to understand how to troubleshoot Kafka clients and identify issues related to throughput, latency, and resource utilization using appropriate observability tools and practices.
Free Confluent CCDAK Exam Actual Questions
Note: Premium Questions for CCDAK were last updated On Nov. 17, 2025 (see below)
To produce data to a topic, a producer must provide the Kafka client with...
All brokers can respond to a Metadata request, so a client can connect to any broker in the cluster and then figure out on its own which brokers to send data to.
A consumer wants to read messages from partitions 0 and 1 of a topic topic1. Code snippet is shown below.
consumer.subscribe(Arrays.asList("topic1"));
List
pc.add(new PartitionTopic("topic1", 0));
pc.add(new PartitionTopic("topic1", 1));
consumer.assign(pc);
subscribe() and assign() cannot be called by the same consumer, subscribe() is used to leverage the consumer group mechanism, while assign() is used to manually control partition assignment and reads assignment
To prevent network-induced duplicates when producing to Kafka, I should use
Producer idempotence helps prevent the network introduced duplicates. More details herehttps://cwiki.apache.org/confluence/display/KAFKA/Idempotent+Producer
What client protocol is supported for the schema registry? (select two)
clients can interact with the schema registry using the HTTP or HTTPS interface
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?
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.
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