Free Confluent CCDAK Practice Exam with Questions & Answers | Set: 2

Log compactionensures that Kafka retains at least thelatest value per keyin a topic. Compaction happensin the backgroundand removes older records with the same keyin inactive log segments, not immediately.

From theKafka Documentation > Log Compaction:

“Kafka guarantees that thelast message for each keywill be retained in the log after compaction, even if earlier messages with the same key are deleted.”

So, D is correct. A is incorrect because compaction does not overwrite; it's a background process. B is incorrect—deletion is not immediate. C incorrectly suggests client-side hashmap behavior.

For real-time applications, thekey metric to monitor is consumer lag— the difference between thelatest offset on the brokerand thelast committed offset by the consumer.

FromKafka Monitoring Guide:

“Consumer lag is the most important metric for real-time applications. It tells you how far behind the consumer is from the latest data in Kafka.”

B relates to replication, not client responsiveness.

C tracks group membership stability, not data delay.

D tracks input throughput, not processing latency.

Comprehensive and Detailed Explanation From Exact Extract:

✅C. Consumers can consume both committed and uncommitted transactions.By default,Kafka consumers only read committed messagesif they are configured with isolation.level=read_committed. However, if configured as read_uncommitted, theycan also consume uncommitted (potentially aborted) transactional messages.

From Kafka Documentation:

"The isolation.level setting controls whether the consumer will read only committed messages or all messages, including uncommitted messages from ongoing or aborted transactions."

✅D. Information about producers and their transactions is stored in the _transaction_state topic.Kafka uses an internal topic named__transaction_stateto maintain metadata about producer transactions. This topic is essential for tracking thetransaction lifecycle, fencing, and recovery.

From Kafka Internals:

“Kafka stores the state of active and completed transactions in an internal topic called __transaction_state.”

Kafka Connect can run instandalone modeordistributed mode. For simple tasks likereading logs from a file on a single host,standalone modeis recommended.

FromKafka Connect User Guide:

“Standalone mode is useful when running connectors on a single machine (e.g., for development or simple deployments like log collection from a local file).”

Distributed mode is preferred for scalability and fault tolerance but overkill for this use case.

The onCompletion() method of Kafka’sCallback interfaceis executedonce the broker acknowledgesthe message. This includes success or failure, and it isinvoked asynchronouslyby the producer.

FromKafka Java Client API Documentation:

“The onCompletion method will be called when the record sent to the server has been acknowledged, or when an error occurs.”

This ensures thatthe record was sent and acknowledged, not just added to a batch or local buffer.

AGlobalKTableis a replicated, read-only table available in full on all instances. It's best forreference or lookup datasetssuch as:

Product catalog

Customer profiles

Warehouse inventory

FromKafka Streams Developer Guide:

“Use GlobalKTable when you need to perform joins using non-partition-aligned reference data that's small enough to replicate.”

Purchases and transactions are high-throughput, time-sensitive streams, not static reference data.

The bootstrap.servers setting is used by clients (producers and consumers) toinitially connectto the Kafka cluster. It does not need to list all brokers—just a few that are up and reachable. The client will then use the cluster metadata to discover the full broker list.

FromKafka Java Client Configuration Docs:

“This is a list of host/port pairs to use for establishing the initial connection to the Kafka cluster. The client will make use of all servers irrespective of which servers are specified here for bootstrapping.”

Hence,listing multiple brokers provides redundancyand fault tolerance in the event one is down during client startup.

For Kafka Streams to performjoins, topics must beco-partitioned, meaning:

They have thesame number of partitions(A), and

They use thesame partitioning strategy(typically by key) (B)

FromKafka Streams Documentation > Joins:

“Co-partitioning is required for Kafka Streams joins. Topics must have the same number of partitions and use the same key-based partitioner.”

Value schema (C) and retention time (D) have no bearing on join compatibility.