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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 Jul. 06, 2026 (see below)
Which of the following setting increases the chance of batching for a Kafka Producer?
linger.ms forces the producer to wait to send messages, hence increasing the chance of creating batches
A producer is sending messages with null key to a topic with 6 partitions using the DefaultPartitioner. Where will the messages be stored?
Message with no keys will be stored with round-robin strategy among partitions.
Which of the following event processing application is stateless? (select two)
Stateless means processing of each message depends only on the message, so converting from JSON to Avro or filtering a stream are both stateless operations
Which of the following Kafka Streams operators are stateful? (select all that apply)
Seehttps://kafka.apache.org/20/documentation/streams/developer-guide/dsl-api.html#stateful-transformations
When using the Confluent Kafka Distribution, where does the schema registry reside?
Schema registry is a separate application that provides RESTful interface for storing and retrieving Avro schemas.
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