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Confluent CCAAK Exam Syllabus Topics:

Topic
Details

Topic 1

• 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 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

• 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.

 

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Confluent Certified Administrator for Apache Kafka Sample Questions (Q18-Q23):

NEW QUESTION # 18
Which out-of-the-box Kafka Authorizer implementation uses ZooKeeper?

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

Answer: D

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 # 19
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 replication factor and increase the number of Connect Tasks.
• C. Increase the number of partitions.
• D. Increase the number of Connect Tasks (tasks max value).

Answer: D

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 # 20
You are managing a cluster with a large number of topics, and each topic has a lot of partitions. A team wants to significantly increase the number of partitions for some topics.
Which parameters should you check before increasing the partitions?

• A. Check if acks=all is beina used.
• B. Check the max open file count on brokers.
• C. Check if compression is being used.
• D. Check the producer batch size and buffer size.

Answer: B

Explanation:
Each Kafka partition maps to multiple log segment files, and each segment results in open file descriptors on the broker. When the number of partitions increases significantly, it can exceed the OS-level limit for open files per broker process, leading to failures or degraded performance. Therefore, it is essential to check and possibly increase the ulimit -n (max open files) setting on the broker machines.

 

NEW QUESTION # 21
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. Consumer Groups
• C. Quotas
• D. ACLs

Answer: C

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 # 22
You want to increase Producer throughput for the messages it sends to your Kafka cluster by tuning the batch size ('batch size') and the time the Producer waits before sending a batch ('linger.ms').
According to best practices, what should you do?

• A. Decrease 'batch.size' and increase 'linger.ms'
• B. Increase 'batch.size' and decrease 'linger.ms'
• C. Increase 'batch.size' and increase 'linger.ms'
• D. Decrease 'batch.size' and decrease 'linger.ms'

Answer: C

Explanation:
Increasing batch.size allows the producer to accumulate more messages into a single batch, improving compression and reducing the number of requests sent to the broker.
Increasing linger.ms gives the producer more time to fill up batches before sending them, which improves batching efficiency and throughput.
This combination is a best practice for maximizing throughput, especially when message volume is high or consistent latency is not a strict requirement.

 

NEW QUESTION # 23
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