Confluent CCDAK Confluent Certified Developer for Apache Kafka Certification Examination Exam Practice Test

RetriableException is a subclass of KafkaException, indicating transient issues thatmight succeed if retried. Examples include:

LeaderNotAvailableException: Happens when metadata is not yet propagated or leader election is in progress.

NotEnoughReplicasException: Happens when the number of in-sync replicas is insufficient. This can be transient if replicas come back online.

From theApache Kafka Java Client documentation:

"These exceptions (like LeaderNotAvailableException, NotEnoughReplicasException) are transient and the client will retry them automatically depending on retry configuration."

RecordTooLargeExceptionandAuthorizationExceptionarenon-retriableas they indicate client-side or permission errors.

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.

BothLeaderNotAvailableExceptionandNotEnoughReplicasExceptionare subclasses of RetriableException, which representstransient issuesthat may succeed upon retry.

FromApache Kafka Java Client Documentation:

“RetriableException indicates that the request can be retried. This includes network errors and certain broker-side failures like leader not available or not enough replicas.”

RecordTooLargeException and AuthorizationException arenon-retriablebecause they represent client misconfigurations or access issues.

fetch-rate: Measures how frequently consumers fetch data — critical for monitoring throughput.

incoming-byte-rate: Shows how much data is being written to the broker per second — a keyproducer-side metric.

FromKafka JMX Metrics Guide:

“fetch-rate, incoming-byte-rate, request-latency are key performance metrics for producers and consumers.”

B, C, E, and F areapplication-layer business metrics, not Kafka performance metrics.

TheStickyAssignortries to minimize partition movement bypreserving existing assignmentsas much as possible while still achieving a balanced assignment. This improvesconsumer stabilityand reduces rebalances.

From theKafka Consumer Assignor Documentation:

“The StickyAssignor attempts topreserve as many existing assignments as possible, which helps minimize partition movement between rebalances.”

RoundRobinAssignor focuses on even distribution, not stability.

RangeAssignor groups partitions by topic and assigns them consecutively, but can lead to imbalances.

PartitionAssignor is an abstract base class, not an assignor used directly.

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.

For proper delivery handling with acks=all:

Usecallbackto log or act on success/failure.

Usetry/catchto handle synchronous exceptions like serialization errors or network failures.

FromKafka Producer Documentation:

“Errors can be caught either via the returned Future or via the callback interface. For fatal errors, use a try/catch block around the send call.”

Option B is incorrect because send() returns a Future, not RecordMetadata directly.

Option D is invalid — ProducerRecord has no method called status().

max.poll.interval.ms defines themaximum delay between invocations of poll()before the consumer is considered failed. It essentially gives consumers more time to process records before needing to poll again.

FromKafka Consumer Configuration Reference:

“max.poll.interval.ms: The maximum delay between invocations of poll() when using consumer group management. If poll() is not called before expiration, the consumer is considered failed.”

session.timeout.ms and heartbeat.interval.ms relate to group coordination and heartbeats.

fetch.max.wait.ms affects how long the broker waits to accumulate data before sending a fetch response.

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.

Kafka Streams usestransactional producersto guaranteeexactly-once semantics (EOS). This ensures that both theoutput recordsandstate store updatesare committed atomically, avoiding duplication or partial writes.

FromKafka Streams Documentation > Processing Guarantees:

“Kafka Streams leveragesKafka’s transactional APIsto commit the output records and internal state updatesas a single atomic unit, thereby providing exactly-once semantics.”

Option A is incorrect because log compaction is not disabled for EOS.

Option C incorrectly describes a checkpointing system Kafka Streams does not use.

Option D refers to deduplication, which is not how EOS is achieved in Streams.

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.

SMTs (Single Message Transforms) arelightweight transformationsapplied to individual messages as they pass through Kafka Connect.

Chaining SMTs: You can applymultiple SMTsin sequence by defining them in order in the connector config.

Masking or modifying fieldsis acommon use case(e.g., redacting sensitive data).

FromKafka Connect Documentation:

“Single Message Transforms (SMTs) are applied to individual messages. You can chain multiple SMTs together.”

SMTsdo not perform joins(B is incorrect).

Converters and SMTs areseparate concerns(D is incorrect).

When using Kafka Connect with sink connectors (like S3 Sink),each task is assigned one or more topic partitions. Thetotal number of partitions (6)isevenly distributedacross the available tasks (2). Thus, each task will handle3 partitions.

FromKafka Connect Documentation:

“Kafka Connect divides the topic partitions among available tasks. For example, a topic with six partitions and two tasks results in each task handling three partitions.”

To send failed records to adead letter queue (DLQ), you must configure:

errors.tolerance=all: Tells the connector tonot failon errors but handle them (e.g., send to DLQ).

errors.deadletterqueue.topic.name=DLQ-Topic: Specifies the DLQ topic.

errors.deadletterqueue.context.headers.enable=true: Includes error context in message headers.

FromKafka Connect Error Handling Docs:

“Kafka Connect supports directing problematic records to a separate topic (DLQ) using errors.* configs. Headers can include failure metadata.”

Options D, E, F are related tologging, not DLQ behavior.

Each of these settings plays a key role inKafka Connect offset management:

offset.flush.interval.ms=60000 → Offset commits happen every 60 seconds.

offset.flush.timeout.ms=500 → The flush operation times out if it takes more than 500 ms.

offset.storage.topic=connect-offsets → This is the internal topic for storing source offsets.

offset.storage.replication.factor=-1 → This uses thebroker's default replication factor(commonly set via default.replication.factor).

FromKafka Connect Docs:

“If offset.storage.replication.factor=-1, the value will be determined by the broker's default replication factor.”

To maintainmessage ordering per bank account, themessage key must be the account number. Kafka guaranteesordering per partition, and the partition is selected based on thehash of the key.

FromKafka Producer Docs:

“All records with the same key will be sent to the same partition, and Kafka preserves the order of records in a partition.”

Using account number ensures:

All messages for the same account go to the same partition

Order is maintained

Workload is evenly distributed if account keys are varied

Other options break ordering or introduce unnecessary randomness.

Kafka clients useSLF4Jas a logging facade. To redirect Kafka logs tolog4j2, you must include theSLF4J binding for log4j2in your classpath:

“org.apache.logging.log4j:log4j-slf4j-impl bridges SLF4J to Log4j2 so that Kafka's logs integrate into your application's Log4j2 configuration.”

Option A is forLog4j 1.x, which is outdated.

Option C is incorrect; Kafka clients don't include logging bindings.

Option D is insufficient without the SLF4J bridge.

unclean.leader.election.enable=false→ Prevents data loss by only considering in-sync replicas when rebalancing.

replication.factor→ Specifies how many redundant copies of partitions are distributed across brokers.

min.insync.replicas=2→ Sets the standard for the number of partition instances that must keep up with the latest committed message.

unclean.leader.election.enable=false ensures that onlyin-sync replicascan be elected as leaders. If disabled, an out-of-sync replica may become leader, potentially leading to data loss.

replication.factor defineshow many brokerswill maintain copies of each partition, directly impacting durability and availability.

min.insync.replicas determineshow many replicas must acknowledgea write when acks=all is used, enforcing write durability.