Confluent CCDAK: Confluent Certified Developer for Apache Kafka (CCDAK)

Last checked November 10, 2026 against the official Confluent exam page.

1. Question 1Kafka core APIs

   A team is building a service that consumes order events and must keep a continuously updated count of orders per customer. The computation has to run inside the service's own process, without a separate processing cluster. Which Kafka API fits this need best?

   1. AKafka Connect API with a sink connector
   2. BKafka Producer API with manual counters kept in application memory
   3. CKafka Admin API with topic configuration
   4. DKafka Streams API with a local state store
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   Correct answer: D

   Kafka Streams is a client library that runs inside the application and supports stateful aggregations through local state stores backed by changelog topics. Connect moves data between Kafka and external systems, the Admin API manages cluster resources, and a bare producer only publishes records with no aggregation support.

2. Question 2Producers and consumers

   A payments service must not lose writes that have been acknowledged, even if a broker fails. Which combination of producer and topic settings best supports this goal?

   1. Aacks=0 with a replication factor of one
   2. Backs=1 with compression turned on for faster batched writes
   3. Cacks=all with min.insync.replicas greater than one
   4. Dacks=all with retries turned off to avoid duplicate writes
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   Correct answer: C

   acks=all waits for the in-sync replicas to hold the record, and min.insync.replicas above one ensures more than one copy exists before the write is acknowledged. acks=0 waits for no broker, acks=1 waits only for the leader, and disabling retries does not add durability.

3. Question 3Deploying real-time streaming applications

   A Kafka Streams application reads a single input topic with a fixed number of partitions. The team starts more application instances to speed up processing. What limits how many tasks can process that input topic in parallel?

   1. AThe number of input topic partitions
   2. BThe number of brokers in the cluster
   3. CThe number of Schema Registry instances
   4. DThe batch.size value on the producer
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   Correct answer: A

   Each stream task processes one partition of the input topic, so the partition count caps useful parallelism. Broker count, Schema Registry instances, and producer batch size do not set the number of tasks for an input topic.

4. Question 4Maintaining streaming applications

   Consumer lag for a group keeps growing, and the team wants to see each partition's committed offset, log end offset, and lag. Which tool provides this view?

   1. AThe kafka-topics tool with its create option to add new topics
   2. BThe kafka-configs tool with its alter option for changing settings
   3. CThe kafka-consumer-groups tool with its describe option
   4. DThe kafka-acls tool with its list option for access rules
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   Correct answer: C

   The consumer groups tool's describe option reports the committed offset, log end offset, and lag per partition for a group. The other tools create topics, change configuration values, and manage access rules, so none of them reports consumer progress.

5. Question 5Kafka core APIs

   Which statement best describes the Kafka Admin API?

   1. AIt moves data between Kafka and external systems through connectors
   2. BIt applies joins, windowed aggregations, and stateful transformations to records in real time
   3. CIt creates, inspects, and alters topics, configs, and consumer groups
   4. DIt publishes records to topics with configurable durability settings
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   Correct answer: C

   The Admin API handles management tasks such as creating topics, altering configurations, and inspecting consumer groups. The other descriptions belong to the Connect API, the Streams API, and the Producer API respectively.