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New Confluent CCAAK Test Notes | CCAAK Intereactive Testing Engine

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Confluent CCAAK Exam Syllabus Topics:

Topic
Details

Topic 1

Apache Kafka® Fundamentals: This section of the exam measures skills of a Kafka Administrator and covers core concepts such as Kafka architecture, components, and data flow. It assesses the candidate’s understanding of topics like topics and partitions, brokers, producers, consumers, and message retention.

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

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 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.

Topic 5

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 6

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.

>> New Confluent CCAAK Test Notes <<

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Confluent Certified Administrator for Apache Kafka Sample Questions (Q39-Q44):

NEW QUESTION # 39
Which statements are correct about partitions? (Choose two.)

A. A partition is comprised of one or more segments on a disk.
B. A partition in Kafka will be represented by a single segment on a disk.
C. All partition segments reside in a single directory on a broker disk.
D. A partition size is determined after the largest segment on a disk.

Answer: A,C

NEW QUESTION # 40
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. 250 GB
C. 75 GB
D. 750 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 # 41
Kafka Connect is running on a two node cluster in distributed mode. The connector is a source connector that pulls data from Postgres tables (users/payment/orders), writes to topics with two partitions, and with replication factor two. The development team notices that the data is lagging behind.
What should be done to reduce the data lag*?
The Connector definition is listed below:
{
"name": "confluent-postgresql-source",
"connector class": "PostgresSource",
"topic.prefix": "postgresql_",
& nbsp;& nbsp;& nbsp;...
"db.name": "postgres",
"table.whitelist": "users.payment.orders",
"timestamp.column.name": "created_at",
"output.data format": "JSON",
"db.timezone": "UTC",
"tasks.max": "1"
}

A. Increase the number of Connect Nodes.
B. Increase the number of Connect Tasks (tasks max value).
C. Increase the number of partitions.
D. Increase the replication factor and increase the number of Connect Tasks.

Answer: B

Explanation:
The connector is currently configured with "tasks.max": "1", which means only one task is handling all tables (users, payment, orders). This can create a bottleneck and lead to lag. Increasing tasks.max allows Kafka Connect to parallelize work across multiple tasks, which can pull data from different tables concurrently and reduce lag.

NEW QUESTION # 42
When using Kafka ACLs, when is the resource authorization checked?

A. The initial time the resource is accessed.
B. Each time the resource is accessed within the configured authorization interval.
C. When the client connection is first established.
D. Each time the resource is accessed.

Answer: D

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 # 43
A topic 'recurring payments' is created on a Kafka cluster with three brokers (broker id '0', '1', '2') and nine partitions. The 'min.insync replicas' is set to three, and producer is set with 'acks' as 'all'. Kafka Broker with id '0' is down.
Which statement is correct?

A. Producers can write messages to all the partitions, because new leaders for the partitions will be elected.
B. Consumers can read committed messages from partitions on broker id T, "2
C. Producers will only be able to write messages to the topic where the Leader for the partition is on Broker id T.
D. Producers and consumers will have no impact on six of the nine partitions.

Answer: D

Explanation:
With 9 partitions spread across 3 brokers, each broker typically hosts 3 leaders (assuming even distribution). When Broker 0 fails, the partitions for which it was leader will elect new leaders on brokers 1 or 2 if enough in-sync replicas (ISRs) remain. But since min.insync.replicas=3 and only 2 brokers are up, no partition can meet the minimum in-sync replica requirement, so producers with acks=all will fail to write. However, for partitions where Broker 0 is not the leader, consumers can still read committed messages. Given that only 3 partitions likely had Broker 0 as leader, six partitions remain accessible for reads, but not writes.

NEW QUESTION # 44
......

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Hugh West Hugh West

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