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Confluent Certified
Administrator for
Apache Kafka
Version: Demo
[ Total Questions: 10]
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CCAAK
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Confluent - CCAAKPass Exam
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A. 
B. 
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D. 
A. 
Category Breakdown
Category Number of Questions
Apache Kafka Security 1
Apache Kafka Cluster Configuration 2
Deployment Architecture 2
Apache Kafka Fundamentals 5
TOTAL 10
Question #:1 - [Apache Kafka Security]
What is the correct permission check sequence for Kafka ACLs?
Super Users # Deny ACL # Allow ACL # Deny
Allow ACL # Deny ACL # Super Users # Deny
Deny ACL # Deny # Allow ACL # Super Users
Super Users # Allow ACL # Deny ACL # Deny
Answer: D
Explanation
Kafka follows a specific sequence for evaluating permissions:
If a user is listed as a Super User # Access granted.
Otherwise, Kafka checks if there's an explicit Allow ACL # Grants access.
If not allowed, it checks for an explicit Deny ACL # Denies access.
If no ACL matches # Denied by default.
From Apache Kafka documentation:
“The evaluation order is: Superuser # Allow ACL # Deny ACL # Default deny.”
Page Reference:
Kafka: The Definitive Guide, 1st Edition, Chapter 9, p. 286
Apache Kafka Documentation: “Kafka Authorization – Evaluation Order”
Question #:2 - [Apache Kafka Cluster Configuration]
Which option is a valid Kafka Topic cleanup policy? (Choose two.)
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A. 
B. 
C. 
D. 
A. 
B. 
C. 
D. 
delete
default
compact
cleanup
Answer: A C
Explanation
Kafka topics support two main cleanup policies:
delete: The default policy where Kafka deletes old log segments based on retention time or size.
compact: Enables log compaction, retaining only the latest value for each key.
From Kafka documentation:
“Valid cleanup.policy values are 'delete' and 'compact'.”
B # Invalid: 'default' is not a recognized cleanup policy.
D # Invalid: 'cleanup' is not a valid keyword in this context.
Page Reference:
Kafka: The Definitive Guide, 1st Edition, Chapter 6, p. 196–197
Apache Kafka Documentation: Topic Configs – cleanup.policy
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Question #:3 - [Deployment Architecture]
Your Kafka cluster has four brokers. The topic t1 on the cluster has two partitions, and it has a replication 
factor of three. You create a Consumer Group with four consumers, which subscribes to t1.
In the scenario above, how many Controllers are in the Kafka cluster?
One
Two
Three
Four
Answer: A
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A. 
B. 
C. 
D. 
E. 
Explanation
Kafka architecture maintains exactly one active Controller at any given time, regardless of the number of 
brokers. The Controller is responsible for administrative tasks like partition leader election and topic creation. 
The remaining brokers are replicas, ready to take over if the Controller fails.
From Kafka documentation:
“At any given time, there is only one active controller in the Kafka cluster.”
Even in a cluster with 4 brokers, only 1 broker acts as the Controller.
Page Reference:
Kafka: The Definitive Guide, 1st Edition, Chapter 6, p. 189
Apache Kafka Documentation: Controller Role Description
##########################################
Question #:4 - [Apache Kafka Fundamentals]
What is the relationship between topics and partitions? (Choose two.)
A topic always has one partition.
A topic may have more than one partition.
A partition is always linked to a single topic.
A partition may have more than one topic.
There is no relationship between topics and partitions.
Answer: B C
Explanation
Kafka topics are logical streams of data, and each topic is split into one or more partitions. Each partition 
belongs to exactly one topic.
From Kafka documentation:
“A topic is a category of messages, split into partitions. Each partition is an ordered, immutable sequence of 
messages, and each belongs to only one topic.”
A # Incorrect: A topic can have more than one partition.
B # Correct: Kafka uses partitions to scale topics.
C # Correct: Partitions belong to exactly one topic.
Confluent - CCAAKPass Exam
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A. 
B. 
C. 
D. 
E. 
F. 
D/E # Incorrect: Invalid Kafka data model.
Page Reference:
Kafka: The Definitive Guide, 1st Edition, Chapter 4, p. 121
Apache Kafka Documentation: Kafka Data Model Overview
##########################################
Question #:5 - [Deployment Architecture]
You are managing a Kafka cluster with five brokers (broker id '0', '1','2','3','4') and three ZooKeepers. There 
are 100 topics, five partitions for each topic, and replication factor three on the cluster. Broker id ‘0’ is 
currently the Controller, and this broker suddenly fails.
Which statements are correct? (Choose three.)
Kafka uses ZooKeeper's ephemeral node feature to elect a controller.
The Controller is responsible for electing Leaders among the partitions and replicas.
The Controller uses the epoch number to prevent a split brain scenario.
The broker id is used as the epoch number to prevent a split brain scenario.
The number of Controllers should always be equal to the number of brokers alive in the cluster.
The Controller is responsible for reassigning partitions to the consumers in a Consumer Group.
Answer: A B C
Explanation
A # Correct: Kafka uses ZooKeeper's ephemeral node (/controller) to elect a single controller. If the 
node disappears (e.g., due to GC pause or failure), a new controller is elected.“Kafka uses an ephemeral 
znode in ZooKeeper to hold the identity of the controller.” — Apache Kafka docs
B # Correct: The controller assigns partition leaders after startup or during rebalancing.
C # Correct: The controller epoch is a monotonically increasing number used to detect outdated 
controller messages.“Kafka uses a controller epoch to avoid split-brain situations. A new controller will 
increase the epoch.”
Incorrect options:
D # Wrong: Broker ID is not used as epoch.
E # Wrong: Only one active controller at a time, not one per broker.
Confluent - CCAAKPass Exam
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A. 
B. 
C. 
D. 
A. 
B. 
F # Wrong: Partition assignment to consumers is handled by the consumer coordinator, not the 
controller.
Page Reference:
Kafka: The Definitive Guide, 1st Edition, Chapter 6, p. 189–192
Apache Kafka Documentation: “Kafka Controller Design”
Question #:6 - [Apache Kafka Fundamentals]
You have a cluster with a topic t1 that already has uncompressed messages. A new Producer starts sending 
messages to t1 with compression enabled.
Which condition would allow this?
If the new Producer is configured to use compression.
Never, because topic t1 already has uncompressed messages.
Only if Kafka is also enabled for encryption.
Only if the new Producer disables batching.
Answer: A
Explanation
Kafka does not enforce a single compression format per topic or partition. Each producer can independently 
choose whether to compress its messages and what compression type to use (e.g., gzip, snappy, lz4). These 
compressed messages coexist with uncompressed ones in the same topic.
From Kafka documentation:“Producers can individually configure compression settings. Kafka stores messages in the format they are 
received, so compressed and uncompressed messages can exist in the same partition.”
Page Reference:
Kafka: The Definitive Guide, 1st Edition, Chapter 5, p. 164–165
Apache Kafka Documentation: Producer Config – compression.type
##########################################
Question #:7 - [Apache Kafka Fundamentals]
How does Kafka guarantee message integrity after a message is written on a disk?
A message can be edited by the producer, producing to the message offset.
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B. 
C. 
D. 
A. 
B. 
C. 
D. 
A message cannot be altered once it has been written.
A message can be grouped with messages sharing the same key to improve read performance.
Only message metadata can be altered using command line (CLI) tools.
Answer: B
Explanation
Kafka ensures message immutability once data is written to disk. It guarantees that the message cannot be 
altered post-write, ensuring strong integrity.
From Kafka documentation:
“Kafka provides strong durability and immutability guarantees. Once a message is written and acknowledged, 
it cannot be modified.”
A # Incorrect: Offsets are immutable. Producers cannot overwrite messages.
C # Partially true for performance but not about message integrity.
D # Metadata changes do not affect message content or integrity.
Page Reference:
Kafka: The Definitive Guide, 1st Edition, Chapter 4, p. 122–123
Apache Kafka Documentation: Message Durability & Immutability
##########################################
Question #:8 - [Apache Kafka Cluster Configuration]
Per customer business requirements, a system’s high availability is more important than message reliability.
Which of the following should be set?
Unclean leader election should be enabled.
The number of brokers in the cluster should be always odd (3, 5, 7 and so on).
The linger.ms should be set to '0'.
Message retention.ms should be set to -1.
Answer: A
Explanation
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A. 
B. 
C. 
D. 
Unclean leader election allows Kafka to elect a non-in-sync replica as a new leader when all in-sync replicas 
are unavailable. This increases availability at the cost of possible message loss (sacrificing reliability).
From Kafka documentation:
“If unclean.leader.election.enable is set to true, a broker that is not in the ISR can be elected as leader, which 
increases availability but may lead to data loss.”
A # Correct: Improves availability when ISR is empty.
B # Best practice for quorum-based systems like ZooKeeper but not the setting that trades off reliability.
C # Controls batching latency, unrelated to availability.
D # Retention -1 means “retain forever,” which impacts storage, not HA.
Page Reference:
Kafka: The Definitive Guide, 1st Edition, Chapter 6, p. 195
Apache Kafka Documentation: Topic Config – unclean.leader.election.enable
##########################################
Question #:9 - [Apache Kafka Fundamentals]
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?
Decrease ‘batch.size’ and decrease ‘linger.ms’
Decrease ‘batch.size’ and increase ‘linger.ms’
Increase ‘batch.size’ and decrease ‘linger.ms’
Increase ‘batch.size’ and increase ‘linger.ms’
Answer: D
Explanation
To increase throughput, Kafka recommends allowing larger batches and giving more time for the producer to 
accumulate records into batches:
batch.size: Increasing this allows more records to be sent in a single request.
linger.ms: Increasing this adds a delay so that the producer can accumulate more messages into a batch.
Confluent - CCAAKPass Exam
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A. 
B. 
C. 
D. 
From Kafka documentation:
“Increasing both batch.size and linger.ms allows the producer to send larger, more efficient batches, 
improving throughput at the cost of slightly increased latency.”
Page Reference:
Kafka: The Definitive Guide, 1st Edition, Chapter 5, p. 162–163
Apache Kafka Documentation: Producer Configuration Parameters
##########################################
Question #:10 - [Apache Kafka Fundamentals]
You have an existing topic t1 that you want to delete because there are no more producers writing to it or 
consumers reading from it.
What is the recommended way to delete the topic?
If topic deletion is enabled on the brokers, delete the topic using Kafka command line tools.
The consumer should send a message with a 'null' key.
Delete the log files and their corresponding index files from the leader broker.
Delete the offsets for that topic from the consumer offsets topic.
Answer: A
Explanation
Kafka supports topic deletion via the kafka-topics.sh tool if the broker property delete.topic.enable=true. This 
is the recommended and cleanest method to delete a topic and its data from the cluster.
From Kafka documentation:
“To delete a topic, use kafka-topics.sh with --delete flag, provided delete.topic.enable is set to true on the 
broker.”
A # Correct: Official method.
B # Misunderstood use of null keys (used for compaction).
C # Not recommended; causes inconsistent state.
D # Deletes consumer state, not the topic itself.
Page Reference:
Kafka: The Definitive Guide, 1st Edition, Chapter 6, p. 198
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Apache Kafka Documentation: Topic Deletion
##########################################
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