CCAAK Exam Dumps - Confluent Certified Administrator Questions and Answers

When a broker is shut down gracefully:

It flushes data from memory to disk to avoid log recovery upon restart.

It also notifies the controller to reassign leadership of the partitions it leads, ensuring high availability.

From Kafka documentation:

“A graceful shutdown will first flush logs to disk and then hand over leadership of partitions to ensure minimal disruption.”

C → Incorrect: Leader election happens only when the controller detects broker loss; it’s not guaranteed on restart.

D → Incorrect: Partition rebalancing is not automatic on broker restart unless manually initiated.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 10, p. 306

Apache Kafka Documentation: Graceful Shutdown Behavior

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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.

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

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Kafka relies on ZooKeeper heartbeats to track broker health. If the broker JVM is unresponsive due to long GC pauses, ZooKeeper won’t receive timely heartbeats and will mark the broker as dead.

From Kafka Documentation:

“If the garbage collection pause is too long, the broker will not send heartbeats to ZooKeeper and will be considered dead. Leadership and partition reassignments will then occur.”

B → Correct: Broker marked dead, causes partition leadership transfers.

A → Incorrect: Log cleaner thread is unrelated to JVM pauses.

C → Incorrect: Clients may be impacted, but this is not the first result.

D → Incorrect: This is a normal OS function, not caused by GC issues.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 10, p. 305

Kafka Official Docs: "ZooKeeper Timeout and Broker Health"

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Confluent Schema Registry supports the following schema types for use with Kafka:

Avro

Protobuf

JSON Schema

These schemas are officially supported for subject versioning, compatibility checks, and REST interface integration.

From Confluent documentation:

“The Confluent Schema Registry supports Avro, JSON Schema, and Protobuf formats.”

Incorrect Options:

gRPC is not supported as a schema format.

Thrift is not supported in Schema Registry.

Page Reference:

Confluent Kafka: The Definitive Guide, 1st Edition, Chapter 7, p. 208–210

Confluent Schema Registry Documentation: Overview Section (Schema Types)

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Apache Kafka supports several security protocols for configuring broker listeners, which define how clients connect to brokers securely or insecurely. As per the official Apache Kafka documentation (“Security Overview” and “Broker Configuration” sections):

PLAINTEXT:“PLAINTEXT – Unauthenticated, unencrypted connections.”This is the default protocol when no security is configured.

SSL:“SSL – SSL authentication and encryption.”It provides secure communication between clients and brokers using SSL/TLS.

SASL_SSL:“SASL_SSL – SASL authentication and SSL encryption.”This combines SASL-based authentication with SSL encryption for secure and authenticated connections.

SASL_PLAINTEXTis also supported, but it was not listed in your options.

Option C (SASL)is not a valid protocol on its own—it’s a mechanism used within SASL_PLAINTEXT or SASL_SSL.

Option E (GSSAPI)is a SASL mechanism (often used with Kerberos), not a protocol.

Therefore, the correct answers are:

A. PLAINTEXT

B. SSL

D. SASL_SSL

Page Reference:

Confluent Kafka: The Definitive Guide, 1st Edition, Chapter 9 (“Kafka Security”), p. 282–283.

Apache Kafka Documentation, “Security Overview” and “Configuring Broker Listeners”.

Kafka quotas allow administrators to throttle producer and consumer throughput to avoid resource starvation. They are based on network and disk usage metrics, applied per client ID or user.

From Apache Kafka documentation:

“Quotas can be configured to control the rate at which data is produced or consumed to avoid one client from overwhelming the system.”

A → Correct: Prevents a single client from degrading cluster performance.

B → Incorrect: Kafka quotas are not for prioritization but fairness.

C → Incorrect: Quotas do not control connection limits.

D → Incorrect: Partition limits are managed via topic configs, not quotas.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 10, p. 306–308

Apache Kafka Documentation: Quotas

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Each Kafka partition is associated with log files and index files that are opened and maintained by the broker. Increasing the number of partitions increases the number of file descriptors (open files). Therefore, it is critical to verify that the broker's system-level max open file count (ulimit -n) is sufficiently high.

From Kafka documentation:

“Each partition uses multiple open files. When the number of partitions increases significantly, make sure to increase the max open file descriptors allowed for the Kafka process.”

C → Correct: Prevents file descriptor exhaustion.

A/B/D → Unrelated to partition count scalability.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 6, p. 192

Apache Kafka Documentation: Production Deployment and Scaling Tips

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For maximum throughput and lowest latency, the setting acks=0 allows the producer to send messages without waiting for any acknowledgment from the broker. This increases speed but at the cost of durability.

From Kafka documentation:

“acks=0: The producer does not wait for any acknowledgment from the broker. The message can be lost but offers the highest throughput.”

A → Correct: Maximizes throughput with no delivery guarantee.

B → Balanced option but not max throughput.

C → Offers strongest durability but lowest throughput.

D → Invalid configuration.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 5, p. 158–159

Apache Kafka Documentation: Producer Configs – acks

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

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In Apache Kafka, rack awareness can be used to ensure better fault tolerance and availability by spreading partition replicas across brokers in different racks. The broker configuration property that explicitly indicates the rack a broker belongs to is broker.rack.

As per the official Apache Kafka documentation:

“To assign a broker to a specific rack, configure the broker.rack property in server.properties. This informs Kafka of the rack location for the broker.”

This is useful for features like rack-aware partition assignment and leader election. With broker.rack set, Kafka can make intelligent decisions to ensure that replicas are distributed across racks.

Other options:

client.rack is a client-side setting to inform Kafka of the client’s rack for fetching from the closest replica, not for brokers.

zookeeper.rack and kafka.rack do not exist in Kafka’s official configuration.

Page Reference:

Apache Kafka Documentation, “Rack Awareness” section.

Confluent Kafka: The Definitive Guide, 1st Edition, Chapter 6 (Kafka Cluster), p. 215.

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