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Valid CCAAK Test Topics & New CCAAK Mock Exam

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Confluent CCAAK Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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 3

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 4

Apache Kafka® Security: This section of the exam measures skills of a Site Reliability Engineer and focuses on securing Kafka environments. It includes authentication mechanisms such as TLS and SASL, authorization using ACLs, and encrypting data at rest and in transit to ensure secure communication and access control.

>> Valid CCAAK Test Topics <<

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Confluent Certified Administrator for Apache Kafka Sample Questions (Q10-Q15):

NEW QUESTION # 10
What are important factors in sizing a ksqlDB cluster? (Choose three.)

A. Number of Partitions
B. Data Encryption
C. Data Schema
D. Topic Data Retention
E. Number of Queries

Answer: A,C,E

Explanation:
The complexity of the schema (number of fields, data types, etc.) affects processing and memory usage.
Each ksqlDB persistent query consumes resources (CPU, memory), so more queries require more capacity.
More partitions increase parallelism, but also resource usage, especially in scaling and state management.

NEW QUESTION # 11
If a broker's JVM garbage collection takes too long, what can occur?

A. There is backpressure to, and pausing of, Kafka clients.
B. There will be a trigger of the broker's log cleaner thread.
C. Log files written to disk are loaded into the page cache.
D. ZooKeeper believes the broker to be dead.

Answer: D

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 # 12
In certain scenarios, it is necessary to weigh the trade-off between latency and throughput. One method to increase throughput is to configure batching of messages.
In addition to batch.size, what other producer property can be used to accomplish this?

A. compression
B. delivery.timeout.ms
C. linger.ms
D. sendbufferbytes

Answer: C

Explanation:
The linger.ms property sets the maximum time the producer will wait before sending a batch, even if the batch isn't full. By increasing linger.ms, you allow more records to accumulate in the batch.

NEW QUESTION # 13
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. client.rack
B. broker.rack
C. zookeeper.rack
D. kafka.rack

Answer: B

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 # 14
When a broker goes down, what will the Controller do?

A. Trigger a leader election among the remaining followers to distribute leadership.
B. Wait for a follower to take the lead.
C. Automatically elect the least loaded broker to become the leader for every orphan's partitions.
D. Become the leader for the topic/partition that needs a leader, pending the broker return in the cluster.

Answer: A

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 # 15
......

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