Pass Your Confluent Certified Administrator CCAAK Exam Easily with Accurate PDF Questions [Jun 30, 2026]
CCAAK Certification Exam Dumps Questions in here
NEW QUESTION # 11
Kafka broker supports which Simple Authentication and Security Layer (SASL) mechanisms for authentication? (Choose three.)
- A. SASL/PLAIN
- B. SASL/OTP
- C. SASL/GSSAPI (Kerberos)
- D. SASL/OAUTHBEARER
- E. SASL/SAML20
Answer: A,C,D
Explanation:
SASL/PLAIN - A simple username/password mechanism supported by Kafka.
SASL/GSSAPI (Kerberos) - Kafka supports Kerberos authentication through the GSSAPI mechanism.
SASL/OAUTHBEARER - Kafka supports OAUTHBEARER for token-based authentication.
NEW QUESTION # 12
You are using Confluent Schema Registry to provide a RESTful interface for storing and retrieving schemas.
Which types of schemas are supported? (Choose three.)
- A. JSON
- B. Protobuf
- C. Thrift
- D. Avro
- E. gRPC
Answer: A,B,D
Explanation:
Avro is the original and most commonly used schema format supported by Schema Registry.
Confluent Schema Registry supports JSON Schema for validation and compatibility checks.
Protocol Buffers (Protobuf) are supported for schema management in Schema Registry.
NEW QUESTION # 13
What is the relationship between topics and partitions? (Choose two.)
- A. There is no relationship between topics and partitions.
- B. Atopic always has one partition.
- C. A partition is always linked to a single topic.
- D. Atopic may have more than one partition.
- E. A partition may have more than one topic.
Answer: C,D
Explanation:
Kafka topics are split into one or more partitions to enable parallelism and scalability.
Each partition belongs to exactly one topic; it cannot span multiple topics.
NEW QUESTION # 14
A Kafka cluster with three brokers has a topic with 10 partitions and a replication factor set to three. Each partition stores 25 GB data per day and data retention is set to 24 hours.
How much storage will be consumed by the topic on each broker?
- A. 75 GB
- B. 300 GB
- C. 750 GB
- D. 250 GB
Answer: B
Explanation:
10 partitions × 25 GB/day = 250 GB total per day for the topic (primary data).
With a replication factor of 3, there are 3 full copies of the data: 250 GB × 3 = 750 GB total across the entire cluster.
The cluster has 3 brokers, and Kafka tries to distribute replicas evenly among them: 750 GB ÷ 3 brokers = 250 GB per broker on average.
However, due to replication, some partitions have leaders and followers, so there's some overlap and not-perfect distribution. Each broker stores approximately 2/3 of the total topic data (since each broker holds replicas for around 2/3 of the partitions).
2/3 × 750 GB = 500 GB, but this is shared, so each broker ends up storing ~300 GB of replicated data, including its share of leaders and followers.
NEW QUESTION # 15
Which option is a valid Kafka Topic cleanup policy? (Choose two.)
- A. default
- B. delete
- C. compact
- D. cleanup
Answer: B,C
Explanation:
The delete policy deletes old log segments when they exceed the retention period or size.
The compact policy retains only the latest record for each key, enabling efficient key-based storage.
NEW QUESTION # 16
Your organization has a mission-critical Kafka cluster that must be highly available. A Disaster Recovery (DR) cluster has been set up using Replicator, and data is continuously being replicated from source cluster to the DR cluster. However, you notice that the message on offset 1002 on source cluster does not seem to match with offset 1002 on the destination DR cluster.
Which statement is correct?
- A. The message was updated on source cluster, but the update did not flow into destination DR cluster and errored.
- B. The offsets for the messages on the source, destination cluster may not match.
- C. The message on DR cluster got over-written accidently by another application.
- D. The DR cluster is lagging behind updates; once the DR cluster catches up, the messages will match.
Answer: B
Explanation:
When using Confluent Replicator (or MirrorMaker), offsets are not preserved between the source and destination Kafka clusters. Messages are replicated based on content, but they are assigned new offsets in the DR (destination) cluster. Therefore, offset 1002 on the source and offset 1002 on the DR cluster likely refer to different messages, which is expected behavior.
NEW QUESTION # 17
How can load balancing of Kafka clients across multiple brokers be accomplished?
- A. Partitions
- B. Replicas
- C. Connectors
- D. Offsets
Answer: A
Explanation:
Partitions are the primary mechanism for achieving load balancing in Kafka. When a topic has multiple partitions, Kafka clients (producers and consumers) can distribute the load across brokers hosting these partitions.
NEW QUESTION # 18
What is the correct permission check sequence for Kafka ACLs?
What is the correct permission check sequence for Kafka ACLs?
- A. Deny ACL -> Deny -> Allow ACL -> Super Users
- B. Super Users -> Allow ACL -> Deny ACL-> Deny
- C. Allow ACL -> Deny ACL -> Super Users -> Deny
- D. Super Users -> Deny ACL -> Allow ACL -> Deny
Answer: B
Explanation:
Kafka checks permissions in the following sequence:
1. Super Users: If the user is a super user (defined via super.users), access is granted immediately.
2. Allow ACL: If there is a matching Allow ACL, Kafka proceeds to the next step.
3. Deny ACL: If there is a matching Deny ACL, access is denied (even if an Allow exists).
4. Deny: If no matching ACLs are found, access is denied by default.
This order ensures that super users bypass ACLs, denials override allows, and default is deny.
NEW QUESTION # 19
Multiple clients are sharing a Kafka cluster.
As an administrator, how would you ensure that Kafka resources are distributed fairly to all clients?
- A. Rebalancing
- B. Quotas
- C. ACLs
- D. Consumer Groups
Answer: B
Explanation:
Kafka quotas allow administrators to control and limit the rate of data production and consumption per client (producer/consumer), ensuring fair use of broker resources among multiple clients.
NEW QUESTION # 20
Which secure communication is supported between the REST proxy and REST clients?
- A. TLS (HTTPS)
- B. MD5
- C. Kerberos
- D. SCRAM
Answer: A
NEW QUESTION # 21
When using Kafka ACLs, when is the resource authorization checked?
- A. Each time the resource is accessed within the configured authorization interval.
- B. Each time the resource is accessed.
- C. The initial time the resource is accessed.
- D. When the client connection is first established.
Answer: B
Explanation:
Kafka ACLs (Access Control Lists) perform authorization checks every time a client attempts to access a resource (e.g., topic, consumer group). This ensures continuous enforcement of permissions, not just at connection time or intervals. This approach provides fine-grained security, preventing unauthorized actions at any time during a session.
NEW QUESTION # 22
Which property in broker configuration specifies that a broker belongs to a particular rack?
Which property in broker configuration specifies that a broker belongs to a particular rack?
- A. kafka.rack
- B. client.rack
- C. broker.rack
- D. zookeeper.rack
Answer: C
Explanation:
The broker.rack property is used in a Kafka broker's configuration to specify the rack or availability zone the broker belongs to. This is important for rack-aware replica placement, allowing Kafka to distribute replicas across different racks for fault tolerance.
NEW QUESTION # 23
When a broker goes down, what will the Controller do?
- A. Wait for a follower to take the lead.
- B. Become the leader for the topic/partition that needs a leader, pending the broker return in the cluster.
- C. Automatically elect the least loaded broker to become the leader for every orphan's partitions.
- D. Trigger a leader election among the remaining followers to distribute leadership.
Answer: D
Explanation:
When a broker goes down, the Controller detects the failure and triggers a leader election for all partitions that had their leader on the failed broker. The leader is chosen from the in-sync replicas (ISRs) of each partition.
NEW QUESTION # 24
If a broker's JVM garbage collection takes too long, what can occur?
- A. There will be a trigger of the broker's log cleaner thread.
- B. Log files written to disk are loaded into the page cache.
- C. ZooKeeper believes the broker to be dead.
- D. There is backpressure to, and pausing of, Kafka clients.
Answer: C
Explanation:
If the broker's JVM garbage collection (GC) pause is too long, it may fail to send heartbeats to ZooKeeper within the expected interval. As a result, ZooKeeper considers the broker dead, and the broker may be removed from the cluster, triggering leader elections and partition reassignments.
NEW QUESTION # 25
Why does Kafka use ZooKeeper? (Choose two.)
- A. To access information about the leaders and partitions
- B. To prevent replication between clusters
- C. For controller election
- D. To scale the number of brokers in the cluster
Answer: A,C
Explanation:
ZooKeeper stores metadata such as partition leadership and ISR (in-sync replicas), which brokers use to coordinate.
Kafka uses ZooKeeper to perform leader election for the Controller broker, which manages cluster metadata and leadership changes.
NEW QUESTION # 26
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