Confluent · CCDAK
Validates proficiency in building applications with Apache Kafka, covering Kafka fundamentals, application development using producer and consumer APIs, Kafka Streams, Kafka Connect, testing, and observability.
Practice Questions
624
≈ 10 practice exams
Duration
90 minutes
Passing Score
70%
Difficulty
AssociateLast Updated
Feb 2026
Use this CCDAK practice exam to prepare for Confluent Certified Developer for Apache Kafka (CCDAK) with realistic questions, detailed explanations, and focused study modes. The practice bank includes 624 questions for Confluent CCDAK, so you can review the exam steadily instead of relying on one long cram session.
As you practice, pay extra attention to patterns in your missed answers. Start with short sessions to identify weak areas, then move into timed quizzes once your accuracy is consistent.
The explanations are especially useful when you want to connect exam wording to the responsibilities and scenarios described in the official certification guidance. Use the free preview first, then unlock the full question bank when you are ready to build a complete study routine.
The Confluent Certified Developer for Apache Kafka (CCDAK) is a vendor-issued certification from Confluent — the company founded by the original creators of Apache Kafka — that validates a developer's ability to build, deploy, and maintain production-grade applications on the Kafka platform. The exam covers the full spectrum of Kafka application development: core architecture, the Producer and Consumer APIs, Kafka Streams for real-time stream processing, Kafka Connect for data integration, Schema Registry with Avro serialization, and application testing and observability practices.
The certification is positioned at the associate level and reflects hands-on proficiency rather than surface-level familiarity. It tests knowledge of delivery semantics (at-most-once, at-least-once, and exactly-once), partition and offset management, serialization strategies, connector configuration, and stream processing topology design. The exam was last updated to align with the current Confluent Platform and covers both Apache Kafka open-source features and Confluent-specific components such as Confluent Schema Registry and ksqlDB basics.
The CCDAK is aimed at software developers, backend engineers, and solutions architects who work with Kafka-based event streaming systems in professional environments. Ideal candidates have 6–12 months of hands-on experience working with Apache Kafka or Confluent Platform and are comfortable reading and writing code in Java, Python, or through RESTful interfaces.
This certification is particularly relevant for engineers building real-time data pipelines, event-driven microservices, or stream processing applications at companies in finance, healthcare, technology, and media — industries where Kafka is commonly deployed at scale. It is also a strong credential for architects who design Kafka-based solutions and need to validate their technical depth to employers or clients.
Confluent does not enforce formal prerequisites for the CCDAK, but the exam assumes 6–12 months of practical experience with Apache Kafka or Confluent Platform. Candidates should be comfortable with core distributed systems concepts — topics, partitions, replication, brokers, and consumer groups — before attempting the exam.
Proficiency in at least one of Java, Python, or a RESTful API client is recommended, as the exam tests application-level knowledge of the Kafka client libraries. Familiarity with the Confluent Schema Registry, Avro serialization, and basic stream processing concepts will also be beneficial. No formal training course or prior Confluent certification is required.
The CCDAK consists of 55 multiple-choice and multiple-select questions delivered in a 90-minute timed session. The exam is fully remote and proctored online, and can be taken from any location worldwide that meets Confluent's internet connectivity, security, and privacy requirements; in-person testing center options are also available globally. The exam costs $150 USD and is valid for two years from the date of passing.
The passing threshold is 70%. Results are provided immediately upon completion. The exam does not include unscored pilot questions in its published format. There is no partial credit on multiple-select questions.
Earning the CCDAK signals verified, hands-on competence to employers in a market where Apache Kafka has become the de facto standard for real-time event streaming. Major technology companies — including Netflix, Uber, Spotify, LinkedIn, and thousands of financial services firms — operate Kafka at scale, creating sustained demand for certified Kafka developers. As of 2024, the average annual salary for Kafka developers in the United States is approximately $125,000, with senior roles and architects earning substantially more. In Europe, salaries range from roughly €57,500–€82,500 in Germany and £70,000–£80,000 in the UK.
The certification is issued by Confluent, the company founded by Kafka's original creators, which gives it strong industry credibility compared to third-party Kafka credentials. It differentiates candidates in hiring processes, supports salary negotiation, and can serve as a stepping stone toward the Confluent Certified Operator for Apache Kafka (CCOAK) or solutions architect roles leading event-driven architecture initiatives. The credential is valid for two years, requiring renewal to stay current with the evolving platform.
5 sample questions with answers and explanations. The full bank has 624 questions, enough for 10 full-length practice exams.
Preview — answers shown1. A development team is implementing error handling for a Kafka Connect sink connector that writes to a database. They configure errors.tolerance=all and errors.deadletterqueue.topic.name=dlq-errors. During operation, the connector fails to start due to an invalid JDBC connection string. What happens to this error? (Select one!)
Explanation
Dead letter queue functionality only applies to record processing errors, not connector initialization or configuration errors. When a connector fails to start due to invalid configuration like a malformed JDBC connection string, the connector enters a FAILED state and logs the error. The DLQ is designed for handling errors during record conversion, transformation (SMT), or sink writing operations. Configuration and startup errors are considered fatal and prevent the connector from reaching the record processing stage. Errors.tolerance=all allows the connector to skip individual bad records during processing but does not apply to initialization failures. The connector must be reconfigured with valid settings before it can start. Understanding the distinction between initialization errors and processing errors is critical for Kafka Connect error handling.
2. A Kafka cluster administrator needs to increase the partition count for a high-throughput topic from 6 to 12 partitions to improve consumer parallelism. The topic currently has replication.factor=3 and contains critical financial transaction data with keys. What consequences should the administrator expect after executing kafka-topics.sh --alter --topic transactions --partitions 12? (Select one!)
Explanation
Increasing partition count changes the partition assignment algorithm for future messages. Since Kafka uses murmur2 hash of the key modulo partition count to determine partition placement, messages with identical keys sent before and after the partition increase will likely map to different partitions. This breaks the ordering guarantee that all messages with the same key are always in the same partition. Existing messages remain in their original partitions and are never redistributed. The operation succeeds regardless of whether messages are keyed. While consumers will rebalance, the fundamental ordering issue persists for keyed data across the old and new partition boundaries.
3. An operations team monitors a Kafka cluster where a topic partition has replication.factor=5 and min.insync.replicas=3. The partition currently has 4 replicas in the ISR set. A producer configured with acks=all sends a message to this partition. How many replicas must acknowledge the write before the producer receives a successful response? (Select one!)
Explanation
With acks=all and min.insync.replicas=3, the producer requires acknowledgment from at least 3 replicas (including the leader) before considering the write successful. The min.insync.replicas configuration sets the minimum number of replicas that must acknowledge writes when acks=all is configured. Even though 4 replicas are currently in ISR, only 3 acknowledgments are required to meet the minimum threshold. The replication.factor defines total replica count but does not determine acknowledgment requirements. If the ISR size falls below min.insync.replicas, producers with acks=all receive NotEnoughReplicasException errors, preventing writes until sufficient replicas rejoin ISR.
4. A developer needs to test a Kafka Streams topology that includes aggregations and windowing operations without connecting to a real Kafka cluster. Which testing approach should be used? (Select one!)
Explanation
TopologyTestDriver is the recommended tool for unit testing Kafka Streams applications without requiring a Kafka cluster. It provides methods to pipe input records through the topology and read output records, while maintaining state stores in memory. The test driver simulates the complete stream processing lifecycle including windowing, joins, and aggregations. EmbeddedKafkaCluster requires running actual Kafka brokers which is slower and more resource-intensive. MockProducer and MockConsumer are for testing basic producer/consumer code, not Streams topologies. KafkaStreamsTest is not a standard Kafka testing class.
5. A Kafka Connect distributed cluster has 3 worker nodes. A connector is configured with tasks.max=5 to ingest data from a source system. How many tasks will actually run in the cluster? (Select one!)
Explanation
The tasks.max configuration specifies the maximum number of tasks the connector can create, but the actual number depends on the connector implementation and the source/sink system characteristics. For example, a JDBC source connector reading from a single table typically creates only 1 task regardless of tasks.max. A Kafka sink connector creates tasks based on the number of topic partitions it consumes. The connector implementation determines how many tasks are needed up to the tasks.max limit. Multiple tasks can run on a single worker, so having 3 workers does not limit task count to 3. The tasks will be distributed across available workers using Kafka Connect's rebalancing protocol.
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