Confluent CCAC: Confluent Cloud Certified Operator (CCAC)

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

1. Question 1Multi-cloud and global Kafka architectures

   A bank runs its primary Kafka cluster in one cloud region and wants a standby cluster in a second cloud provider that can accept writes within minutes during a regional outage. Which design best meets this requirement?

   1. ARun a nightly script that exports topic contents to object storage and reloads them into a new cluster after an outage
   2. BHave every producer write to both clusters at the same time, with no replication between the two clusters
   3. CMirror the topics to the standby cluster with cluster linking, then fail over the mirror topics so they become writable
   4. DKeep only the primary cluster and have producers retry until the original region comes back online
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   Correct answer: C

   Cluster linking keeps mirror topics on the standby cluster continuously synchronized, and failing them over makes them writable within minutes. The nightly export has a recovery point of up to a day, simultaneous dual writes create divergent copies, and waiting for the region to recover does not meet the requirement.

2. Question 2Cluster Linking

   Which statement about a mirror topic created by cluster linking is accurate?

   1. AA mirror topic can accept direct writes from local producers at any time, which is why it suits active-active designs
   2. BA mirror topic on the destination is read-only and follows the source until it is promoted to a regular writable topic
   3. CA mirror topic copies only recent records from the source and stops replicating older data that is still retained there
   4. DA mirror topic depends on a Kafka Connect source connector that must be recreated after every source broker restart
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   Correct answer: B

   Mirror topics are read-only on the destination while they replicate from the source, and promoting one turns it into a regular writable topic. Mirror topics do not accept producer writes, do not copy only recent data, and do not rely on Kafka Connect source connectors.

3. Question 3Fully managed connectors

   A team needs to stream row-level changes from a PostgreSQL database into Confluent Cloud, and does not want to operate any Kafka Connect workers itself. Which option should the team choose?

   1. AA self-managed Debezium connector running on a Connect cluster that the team installs on its own virtual machines
   2. BA cron job that queries the database every minute and produces the new rows with a custom producer client
   3. CA Kafka Streams application that polls the source tables for new rows and writes each one to a topic
   4. DA fully managed PostgreSQL CDC Source connector that Confluent Cloud runs on its own managed infrastructure
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   Correct answer: D

   The fully managed PostgreSQL CDC Source connector runs on Confluent-managed infrastructure, so the team does not operate Connect workers. The self-managed Debezium option needs the team to run workers, and polling approaches miss row-level changes such as deletes while adding operational work.

4. Question 4Stream Governance

   A producer team wants to add a new field named loyalty_tier to an Avro schema whose subject uses BACKWARD compatibility. Consumers must still be able to read both old and new records. Which change is compatible?

   1. AChange the data type of the existing amount field from int to string so that values can hold decimal text
   2. BRename the existing customer_id field to customerId and remove the old name without adding an alias
   3. CAdd loyalty_tier as a required field with no default, so that every new record must include it
   4. DAdd loyalty_tier with a default value, so a reader using the new schema can still decode older records
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   Correct answer: D

   Under BACKWARD compatibility the new schema must read data written with the previous schema, so a new field needs a default value. A required field without a default, a type change, or a rename without an alias all break the reading of older records.

5. Question 5Stream processing

   An analyst needs the number of orders per customer in every five-minute interval, where the intervals never overlap and each event is counted in exactly one interval. Which window type should the stream processing job use?

   1. AA hopping window, which uses fixed-size intervals that advance by a smaller step so that they overlap
   2. BA tumbling window, which uses fixed-size, non-overlapping intervals so each event falls into one window
   3. CA global window, which keeps all events for a key in one unbounded group until a custom trigger fires
   4. DA session window, which groups the events of each key that are separated by less than a chosen inactivity gap
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   Correct answer: B

   A tumbling window uses fixed, non-overlapping intervals, so each event belongs to exactly one window. Hopping windows overlap, session windows depend on inactivity gaps rather than fixed intervals, and global windows do not split events by time.