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Avail Professional Visual CCAAK Cert Exam to Pass CCAAK on the First Attempt

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Confluent CCAAK Exam Syllabus Topics:

Topic
Details

Topic 1

Apache Kafka® Cluster Configuration: This section of the exam measures skills of a Kafka Administrator and includes configuring broker properties, tuning for performance, managing topic-level settings, and applying best practices for production-grade environments.

Topic 2

Observability: This section of the exam measures skills of a Site Reliability Engineer and focuses on monitoring Kafka clusters. It assesses knowledge of metrics, logging, and alerting tools, including how to use them to maintain cluster health and performance visibility.

Topic 3

Troubleshooting: This section of the exam measures skills of a Kafka Administrator and includes diagnosing common issues in Kafka clusters. It covers problem areas such as performance bottlenecks, message delivery failures, replication issues, and consumer lag, along with techniques to resolve them effectively.

Topic 4

Deployment Architecture: This section of the exam measures skills of a Kafka Administrator and covers different Kafka deployment topologies. It evaluates understanding of high availability, disaster recovery, multi-data center replication, and the roles of Zookeeper or KRaft in the cluster architecture.

Topic 5

Kafka Connect: This section of the exam measures skills of a Site Reliability Engineer and addresses the use and management of Kafka Connect for data integration. It includes setting up connectors, managing configurations, and ensuring efficient movement of data between Kafka and external systems.

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Confluent Certified Administrator for Apache Kafka Sample Questions (Q41-Q46):

NEW QUESTION # 41
Which out-of-the-box Kafka Authorizer implementation uses ZooKeeper?

A. LDAP
B. RBAC
C. ACLs
D. Ranger

Answer: C

Explanation:
Kafka's built-in ACL (Access Control List) authorizer stores and manages permissions using ZooKeeper by default. This implementation controls access at the resource level (topics, consumer groups, etc.).

NEW QUESTION # 42
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 on DR cluster got over-written accidently by another application.
B. The message was updated on source cluster, but the update did not flow into destination DR cluster and errored.
C. The offsets for the messages on the source, destination cluster may not match.
D. The DR cluster is lagging behind updates; once the DR cluster catches up, the messages will match.

Answer: C

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 # 43
Which of the following are Kafka Connect internal topics? (Choose three.)

A. connect-confiqs
B. connect-offsets
C. connect-standalone
D. connect-distributed
E. connect-status

Answer: A,B,E

Explanation:
connect-configs stores connector configurations.
connect-status tracks the status of connectors and tasks (e.g., RUNNING, FAILED).
connect-offsets stores source connector offsets for reading from external systems.

NEW QUESTION # 44
How can load balancing of Kafka clients across multiple brokers be accomplished?

A. Replicas
B. Partitions
C. Connectors
D. Offsets

Answer: B

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 # 45
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. 300 GB
B. 75 GB
C. 750 GB
D. 250 GB

Answer: A

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 # 46
......

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