Free Practice Questions for Confluent CCAAK Exam

Event stream processing is ideal for real-time and near-real-time scenarios. Kafka Streams and ksqlDB are commonly used in use cases requiring immediate reaction to data.

A → Correct: Fraud detection systems benefit from low-latency anomaly detection.

B → Correct: Real-time product recommendations based on user activity require continuous processing.

C → Incorrect: End-of-day processing is batch-oriented, not real-time.

D → Correct: Real-time alerting and log anomaly detection are classic streaming use cases.

E → Incorrect: Historical dashboards rely on batch aggregation and are not continuous processing.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 7, p. 210–215

Confluent Documentation: Use Cases for Kafka Streams and ksqlDB

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When sizing a ksqlDB cluster, the following factors are crucial:

A. Data Schema: Complex schemas may require more CPU/memory for serialization/deserialization.

B. Number of Queries: Each persistent query consumes system resources (CPU, memory).

C. Number of Partitions: Determines parallelism and state store scalability.

From Confluent documentation:

“Cluster capacity planning depends on schema complexity, query concurrency, and partition count.”

D → Incorrect: Encryption impacts producer/broker, not ksqlDB sizing directly.

E → Incorrect: Retention is managed by Kafka, not ksqlDB.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 7, p. 225–229

Confluent ksqlDB Documentation: Capacity Planning Guidelines

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Apache Kafka's built-in ACL mechanism (SimpleAclAuthorizer) stores ACL data in ZooKeeper. ACLs are propagated to brokers from ZooKeeper and cached in memory for fast lookup.

From Kafka documentation:

“ACLs are stored in ZooKeeper under /kafka-acl and are loaded into each broker at startup and on updates.”

B → Correct: ZooKeeper stores ACLs centrally.

A → Brokers load ACLs but don’t persist them.

C & D → Do not store or manage ACLs.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 9, p. 285

Apache Kafka Documentation: Authorizer Configuration

In Kafka, a Leader ID of -1 indicates that no broker is currently the leader for that partition. This typically happens when the only replica for the partition (broker 1 in this case) is offline or unavailable.

From Kafka documentation:

“If the leader for a partition is not available, the leader ID will be reported as -1.”

A → Correct: The broker hosting the sole replica is down.

B/C/D → Incorrect and unrelated to leader election behavior.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 6, p. 191

Apache Kafka Documentation: Topic Description Output and Leader Election

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In Kafka, the Controller is responsible for managing partition leadership. When a broker that was leader for certain partitions goes down, the Controller immediately detects the failure and:

Triggers a new leader election among the in-sync replicas (ISRs) of those partitions.

Updates the partition metadata to reflect the new leaders and notifies the cluster.

Kafka documentation states:

“When a broker fails, the controller triggers a leader election for all partitions previously led by the failed broker.”

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 6, p. 190–191

Apache Kafka Documentation: “Kafka Controller and Leader Election”

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Kafka’s default authorizer implementation is the SimpleAclAuthorizer, which stores ACLs in ZooKeeper and reads them on each access.

From Kafka documentation:

“Apache Kafka includes an out-of-the-box authorizer implementation that stores ACLs in ZooKeeper.”

A → RBAC is Confluent-specific and not stored in ZooKeeper.

B → Correct: Built-in ACL mechanism uses ZooKeeper.

C → Ranger is an external plugin, not bundled with Apache Kafka.

D → LDAP is used for authentication, not authorization.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 9, p. 285

Apache Kafka Documentation: ACLs and Authorizers

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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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Replication in Kafka ensures that partition data is copied across multiple brokers. This guarantees durability (data survives broker failure) and availability (other replicas can serve as leader if current leader fails).

From Kafka documentation:

“Replication ensures that data is not lost if a broker crashes and that messages remain available even during broker outages.”

B → Correct: Ensures persisted data even after failure.

C → Correct: Allows partition leader failover.

A → Incorrect: Replication may slightly reduce performance.

D → Incorrect: Replication is not a security mechanism.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 6, p. 189–191

Apache Kafka Documentation: Replication 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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