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Confluent CCDAK Exam Syllabus Topics:

Section	Objectives
Topic 1: Kafka Streams and Processing	- Kafka Streams API 1. Stateful vs stateless processing 2. Stream processing fundamentals - Event processing patterns 1. Exactly-once semantics 2. Windowing and aggregation
Topic 2: Kafka Ecosystem and Confluent Platform	- Schema Registry 1. Avro, Protobuf, JSON Schema usage - ksqlDB and connectors 1. Kafka Connect basics 2. Stream querying concepts
Topic 3: Kafka Producers and Consumers	- Consumer API 1. Rebalancing and offset commits 2. Consumer groups - Producer API 1. Serialization and partitioning strategies 2. Reliability and acknowledgments
Topic 4: Kafka Fundamentals	- Kafka architecture and core concepts 1. Replication and fault tolerance 2. Brokers, topics, partitions - Data flow in Kafka 1. Producer and consumer model 2. Offset management

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Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions (Q31-Q36):

NEW QUESTION # 31
Refer to the producer code below. It features a 'Callback' class with a method called 'onCompletion()'. When will the 'onCompletion()' method be Invoked?

producer.send(record, new MyCallback(record));

A. When the producer batches the message
B. When the producer puts the message into its socket buffer
C. When a broker sends an acknowledgement to the producer
D. When a consumer sends an acknowledgement to the producer

Answer: C

NEW QUESTION # 32
Your configuration parameters for a Source connector and Connect worker are:
* offset.flush.interval.ms=60000
* offset.flush.timeout.ms=500
* offset.storage.topic=connect-offsets
* offset.storage.replication.factor=-1Which four statements match the expected behavior?(Select four.)

A. The connector will commit offsets to a topic called connect-offsets.
B. The offsets topic will use the broker default replication factor.
C. The connector will wait 60000ms before trying to commit offsets for tasks.
D. The connector will wait 500ms for offset data to be committed.

Answer: A,B,C,D

Explanation:
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." Reference:Kafka Connect Worker Configuration

NEW QUESTION # 33
(A consumer application needs to use an at-most-once delivery semantic.
What is the best consumer configuration and code skeleton to avoid duplicate messages being read?)

A. auto.offset.reset=latest and enable.auto.commit=truewhile (true) {final var records = consumer.poll (POLL_TIMEOUT);for (var record : records) {// Any processing}consumer.commitAsync();}
B. auto.offset.reset=earliest and enable.auto.commit=falsewhile (true) {final var records = consumer.poll (POLL_TIMEOUT);consumer.commitAsync();for (var record : records) {// Any processing}}
C. auto.offset.reset=earliest and enable.auto.commit=truewhile (true) {final var records = consumer.poll (POLL_TIMEOUT);consumer.commitAsync();for (var record : records) {// Any processing}}
D. auto.offset.reset=earliest and enable.auto.commit=falsewhile (true) {final var records = consumer.poll (POLL_TIMEOUT);for (var record : records) {// Any processing}consumer.commitAsync();}

Answer: B

Explanation:
According to the official Apache Kafka consumer semantics documentation, at-most-once delivery means that records may be lost but must never be processed more than once. To achieve this guarantee, a consumer must commit offsets before processing records. If the application crashes after committing but before processing, messages are skipped rather than reprocessed, which satisfies at-most-once semantics.
Option B correctly implements this pattern. It disables automatic offset commits (enable.auto.commit=false) and explicitly calls commitAsync() immediately after polling and before processing records. This ensures that offsets are advanced regardless of whether processing succeeds, eliminating the possibility of duplicate processing.
Option C represents at-least-once semantics, because offsets are committed after processing, which can lead to duplicates on failure. Options A and D rely on automatic commits or incorrect ordering and do not provide strict at-most-once guarantees.
The Kafka documentation explicitly states that manual offset management with commits before processing is required for at-most-once delivery.
Therefore, Option B is the best and officially documented configuration and code skeleton to avoid duplicate message processing.

NEW QUESTION # 34
You create a topic named loT-Data with 10 partitions and replication factor of three.
A producer sends 1 MB messages compressed with Gzip.
Which two statements are true in this scenario?
(Select two.)

A. All compressed messages will be stored in the same topic partition.
B. Compression type will be stored in batch attributes.
C. By default, compression is the producer's responsibility.
D. The message is already compressed so it will not be serialized.

Answer: B,C

Explanation:
* A:Kafka batches storecompression metadata in their headers, indicating how the batch was compressed.
* B:Compression is controlled via theproducer config compression.type, which defaults to none.
Producers explicitly set Gzip or other formats.
FromKafka Producer Docs:
"Compression is done by the producer, and the type is stored in the message batch metadata. The broker does not recompress."
* C is false: messages are serializedbeforecompression.
* D is false: messages without keys are distributed acrossmultiple partitions.
Reference:Kafka Producer Configs > compression.type

NEW QUESTION # 35
You have a consumer group with default configuration settings reading messages from your Kafka cluster.
You need to optimize throughput so the consumer group processes more messages in the same amount of time.
Which change should you make?

A. Disable auto commit and have the consumers manually commit offsets.
B. Increase the number of bytes the consumers read with each fetch request.
C. Decrease the session timeout of each consumer.
D. Remove some consumers from the consumer group.

Answer: B

Explanation:
To increase consumer throughput, one effective strategy is to increase the amount of data fetched in each poll by raising fetch.max.bytes or max.partition.fetch.bytes. This allows each poll to retrieve more records per request, improving processing efficiency.
From Kafka Consumer Config Docs:
"Increasing fetch size allows consumers to retrieve larger batches of messages, improving throughput and reducing request overhead." Removing consumers (A) may reduce parallelism.
Manual commit (C) adds complexity, not throughput.
Decreasing session timeout (D) risks unnecessary rebalances.
Reference: Kafka Consumer Configuration > fetch.max.bytes

NEW QUESTION # 36
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