Confluent · CCAAK
Validates expertise in managing Apache Kafka clusters in production, covering Kafka fundamentals, cluster configuration, security, deployment architecture, Kafka Connect administration, observability, and troubleshooting.
Practice Questions
624
≈ 10 practice exams
Duration
90 minutes
Passing Score
70%
Difficulty
AssociateLast Updated
Feb 2026
Use this CCAAK practice exam to prepare for Confluent Certified Administrator for Apache Kafka (CCAAK) with realistic questions, detailed explanations, and focused study modes. The practice bank includes 624 questions for Confluent CCAAK, 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 Administrator for Apache Kafka (CCAAK) is a professional certification that validates the skills required to deploy, configure, monitor, and maintain Apache Kafka clusters in production environments. It covers a broad spectrum of administrative competencies, including broker and topic configuration, ZooKeeper management, security implementation (SSL/TLS, SASL, ACLs), Kafka Connect administration, Schema Registry, observability practices, and production troubleshooting. The certification is offered by Confluent, the company founded by the original creators of Apache Kafka, and is recognized across the industry as a credible benchmark for Kafka operations expertise.
The exam tests both conceptual understanding and scenario-based knowledge, requiring candidates to demonstrate proficiency with real-world challenges such as managing consumer group rebalances, diagnosing replication health, configuring listener protocols correctly, and resolving consumer lag. Candidates must understand the roles of brokers, leaders, group coordinators, and how components like Kafka Streams and ksqlDB interact within the broader ecosystem. The certification expires after two years, requiring recertification to remain current.
The CCAAK is designed for professionals who are responsible for the day-to-day administration and operation of Apache Kafka clusters. This includes platform engineers, site reliability engineers (SREs), DevOps engineers, and infrastructure administrators who manage Kafka in self-managed, Kubernetes-based, or cloud-hosted environments. Candidates typically have hands-on experience with Kafka CLI tooling and configuration files, and are comfortable diagnosing issues such as replication lag, consumer timeouts, and partition imbalances.
The certification is well-suited for professionals who want to formalize their Kafka administration skills and distinguish themselves in the job market. It is not intended for developers building Kafka-based applications (who would be better served by the CCDAK developer certification), but rather for those responsible for the health, security, and operational performance of Kafka infrastructure.
Confluent does not enforce formal prerequisites to register for the CCAAK exam. However, candidates are strongly recommended to have practical, hands-on experience running Kafka in a production or production-like environment before attempting the exam. This includes comfort with broker configuration files, CLI tools (kafka-topics, kafka-consumer-groups, kafka-configs, etc.), and experience troubleshooting common operational issues such as under-replicated partitions, consumer lag, and connectivity errors.
Familiarity with Kafka's core components — brokers, producers, consumers, consumer groups, ZooKeeper (or KRaft mode), Kafka Connect, and Schema Registry — is essential. Confluent recommends reviewing the official online study guide and, optionally, completing their formal training courses (available in live and self-paced formats) prior to sitting the exam. Most candidates report studying between 30 and 120 hours depending on their existing Kafka experience.
The CCAAK is a 90-minute, proctored, multiple-choice exam delivered online or at authorized testing centers worldwide. The exam consists of multiple-choice and multi-select questions, with the total question count reported at approximately 40–60 questions depending on the exam version. Remote delivery requires a webcam for proctor monitoring throughout the session. The exam is administered in English only, and results are displayed immediately upon completion.
The passing score is 70%, and the cost per attempt is $150 USD. The certification is valid for two years, after which recertification is required. Upon passing, candidates receive a Confluent digital badge and certificate and are authorized to use the certification title and logo in professional materials.
Earning the CCAAK demonstrates verified expertise in Apache Kafka administration, a skill set in high demand as organizations across financial services, e-commerce, telecommunications, and technology sectors scale their event streaming infrastructure. Kafka administrators and platform engineers with this credential are well-positioned for roles such as Senior Kafka Administrator, Platform Engineer, Data Infrastructure Engineer, and Site Reliability Engineer. The certification serves as a credible differentiator in both salary negotiations with current employers and job applications with new ones.
Apache Kafka skills consistently command above-average compensation in the data engineering and platform engineering space, with experienced Kafka administrators in North America typically earning $130,000–$180,000+ USD annually. The CCAAK pairs well with cloud certifications (AWS, GCP, Azure) and complements the Confluent Certified Developer for Apache Kafka (CCDAK) for professionals seeking full-stack Kafka expertise. As organizations increasingly adopt event-driven architectures, demand for credentialed Kafka operators continues to grow.
5 sample questions with answers and explanations. The full bank has 624 questions, enough for 10 full-length practice exams.
Preview — answers shown1. A Kafka Connect distributed cluster runs with three workers. An administrator creates a JDBC source connector with tasks.max=8 to read from eight database tables. After deployment, the connector status shows RUNNING but only three tasks are created. What is the MOST likely explanation? (Select one!)
Explanation
In Kafka Connect distributed mode, the framework distributes tasks across available workers in the cluster. While tasks.max=8 requests eight tasks, the connector can only create as many tasks as can be effectively distributed across workers. With three workers, the typical behavior is to create tasks up to a multiple of worker count or based on connector-specific limitations. Many connectors, including JDBC source connectors, may limit task creation based on available parallelism or resource constraints. In this case, the connector created only three tasks, likely one per worker, because the connector implementation determined that only three tasks could be meaningfully parallelized across the three workers. Kafka Connect does not have a hard limit of one task per worker. The tasks.max setting is not ignored in distributed mode. JDBC connectors can create multiple tasks from a single connection pool configuration.
2. A data pipeline team configures a Kafka Connect sink connector to write Kafka topic data to an external database. The connector is configured with errors.tolerance=all and errors.deadletterqueue.topic.name=failed-records-dlq. After deployment, the team observes that when the database becomes temporarily unavailable, records are not written to the dead letter queue, and the connector continues retrying indefinitely. Which configuration explains this behavior? (Select one!)
Explanation
The errors.tolerance configuration in Kafka Connect controls error handling for transformation and conversion errors during record processing, not for errors during the sink write operation to external systems. When a sink connector fails to write to the target system due to connectivity issues, database errors, or other external failures, these are considered task-level failures rather than record-level processing errors. Kafka Connect will retry these operations according to the connector's internal retry logic, but will not route records to the dead letter queue. The DLQ is specifically designed for records that fail during deserialization (conversion from Kafka format) or during Single Message Transform (SMT) execution. The errors.deadletterqueue.context.headers.enable setting controls whether error context metadata is written as headers on DLQ records but does not affect whether records are sent to the DLQ. The errors.retry.timeout parameter controls how long Connect retries retriable errors before failing, but does not change which error types are routed to the DLQ versus causing task failures. The errors.deadletterqueue.topic.replication.factor setting controls DLQ topic durability but does not affect when records are written to the DLQ.
3. A retail company runs Kafka Connect in distributed mode for ingesting data from multiple databases. The administrator needs to verify that the internal topics used by Kafka Connect have sufficient replication to survive broker failures. Which three internal topics must be checked for proper replication factor configuration? (Select three!)
Multiple correct answersExplanation
Kafka Connect in distributed mode uses three internal topics: connect-configs for storing connector configurations, connect-offsets for tracking source connector positions, and connect-status for storing connector and task status information. These topics should have replication factors matching the cluster's durability requirements, typically 3 in production. The __consumer_offsets and __transaction_state topics are Kafka internal topics, not Connect-specific. There is no connect-metadata topic in Kafka Connect.
4. An administrator configures a topic with unclean.leader.election.enable=false and min.insync.replicas=2. The topic has a replication factor of 3. During a network partition, two replicas including the leader become isolated from the third replica. The isolated leader continues receiving writes with acks=all from producers. When the network partition resolves, what is the expected behavior? (Select one!)
Explanation
When the isolated leader has two replicas in its partition including itself, it can still satisfy min.insync.replicas=2 and continue accepting writes with acks=all. When the network partition resolves, the original leader retains leadership because it never lost the ISR quorum. The third replica that was isolated will rejoin the ISR and truncate its log to match the current leader's log, discarding any divergent data. The third replica cannot be elected leader during partition because unclean.leader.election.enable=false prevents non-ISR replicas from becoming leaders. Writes to the isolated leader are not lost because the ISR requirement was satisfied throughout. Manual intervention is not required as normal leader election and ISR synchronization processes handle partition recovery automatically.
5. A streaming platform uses ksqlDB with processing.guarantee=exactly_once_v2. Which two Kafka features are automatically leveraged to provide exactly-once semantics? (Select two!)
Multiple correct answersExplanation
Exactly-once processing in ksqlDB and Kafka Streams relies on two core mechanisms. Idempotent producers use sequence numbers and producer IDs to ensure each message is written exactly once even with retries. Transactions group multiple operations including writing output records and committing input offsets into atomic units that either fully succeed or fully fail, preventing partial updates. Static membership helps reduce rebalance disruptions but does not provide exactly-once guarantees. Log compaction is for key-based deduplication, not transactional semantics. Synchronous replication provides durability but not exactly-once processing guarantees.
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