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Apache Kafka is a popular open-source, distributed streaming platform that is widely used for handling real-time data streaming in modern enterprises. It has been gaining rapid acceptance for its ability to handle massive amounts of data and support different workloads. Many businesses are now leveraging Apache Kafka to enable streaming data processing in their organizations, making it essential for developers to become proficient in using Kafka to build, manage and maintain these applications.
The CCDAK certification exam is offered by Confluent, which is the company behind the popular open-source Apache Kafka project. CCDAK exam is designed to test developers' knowledge of Kafka concepts, including Kafka architecture, producers and consumers, message delivery semantics, and Kafka Streams. CCDAK exam is also designed to test developers' ability to build and manage Kafka clusters and write efficient Kafka applications.
NEW QUESTION # 12
What will happen if a producer tries to send messages to a topic that does not exist in the Kafka cluster?
- A. The messages will be sent to a topic called unnamed_topic_1.
- B. The brokers will hold the messages until a user creates a topic for the messages.
- C. The Producer "send()" call will block indefinitely.
- D. Messages will only be sent if auto topic creation is enabled.
Answer: D
NEW QUESTION # 13
How would you describe a connector in ksqlDB?
- A. DROP CONNECTOR [IF EXISTS] connector_name;
- B. DESCRIBE connector_name CONNECTOR;
- C. DESCRIBE CONNECTOR connector_name;
- D. DESCRIBE ip_sum EXTENDED;
Answer: C
NEW QUESTION # 14
(You are building real-time streaming applications using Kafka Streams.
Your application has a custom transformation.
You need to define custom processors in Kafka Streams.
Which tool should you use?)
- A. TopologyTestDriver
- B. Kafka Streams Custom Transformation Language
- C. Kafka Streams Domain Specific Language (DSL)
- D. Processor API
Answer: D
Explanation:
The Apache Kafka Streams documentation clearly distinguishes between the Kafka Streams DSL and the Processor API. While the DSL is designed for common stream processing operations such as filtering, mapping, joining, and aggregations, it does not support fully custom processing logic.
For use cases that require custom transformations, fine-grained control over record processing, access to headers, timestamps, state stores, and punctuation, Kafka Streams provides the Processor API. This API allows developers to implement custom Processor, Transformer, or ValueTransformer classes and explicitly define the processing topology.
Option A (TopologyTestDriver) is a testing utility, not a development API. Option C (DSL) is higher-level and not suitable for advanced custom logic. Option D does not exist in Kafka.
Therefore, the correct and officially supported approach for defining custom processors in Kafka Streams is to use the Processor API.
NEW QUESTION # 15
Which two statements about Kafka Connect Single Message Transforms (SMTs) are correct?
(Select two.)
- A. SMTs are often used to join multiple records from a source data system into a single Kafka record.
- B. SMT functionality is included within Kafka Connect converters.
- C. Masking data is a good example of an SMT.
- D. Multiple SMTs can be chained together and act on source or sink messages.
Answer: C,D
Explanation:
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).
Reference:Kafka Connect Transformations Guide
NEW QUESTION # 16
Which two statements are correct when assigning partitions to the consumers in a consumer group using the assign() API?
(Select two.)
- A. It is mandatory to subscribe to a topic before calling assign() to assign partitions.
- B. The consumer chooses which partition to read without any assignment from brokers.
- C. The consumer group will not be rebalanced if a consumer leaves the group.
- D. All topics must have the same number of partitions to use assign() API.
Answer: B,C
Explanation:
Using assign() givesmanual controlover partition assignment. In this mode:
* B is true: The applicationchooses partitionsdirectly without involving the group coordinator.
* C is true: Since no group coordination occurs,no rebalancinghappens if a consumer dies.
FromKafka Consumer API Docs:
"assign() manually assigns a list of partitions to this consumer. No coordination takes place and no rebalancing is triggered."
* A is false: subscribe() and assign() aremutually exclusive.
* D is incorrect: Partitions don't need to be equal across topics.
Reference:Kafka Consumer Java API > assign() and subscribe()
NEW QUESTION # 17
(You want to read messages from all partitions of a topic in every consumer instance of your application.
How do you do this?)
- A. Use the assign() method with the topic name as argument.
- B. Use the assign() method using all topic-partitions of the topic as argument.
- C. Use the subscribe() method with a regular expression argument.
- D. Use the subscribe() method with an empty consumer group name configuration.
Answer: B
Explanation:
According to the Apache Kafka Consumer API documentation, the assign() method allows a consumer to manually assign specific partitions to itself, bypassing Kafka's consumer group coordination and rebalancing mechanism. When a consumer uses assign() with all partitions of a topic, that consumer will read from every partition, regardless of how many other consumers exist.
This behavior is required when every consumer instance must independently read the full dataset, such as in auditing, monitoring, or replication use cases. In contrast, subscribe() is designed for consumer group usage, where partitions are distributed among consumers, ensuring that each partition is consumed by only one consumer in the group.
Option B is invalid because assign() requires a collection of TopicPartition objects, not just a topic name.
Option C still uses group coordination and does not guarantee all partitions per consumer. Option D is invalid because an empty group ID is not supported by Kafka.
Thus, explicitly calling assign() with all topic partitions is the only correct and documented way to ensure each consumer reads from all partitions.
NEW QUESTION # 18
You are working on an Orders microservice Thai is publishing messages lo an Orders topic. II is a business requirement that the Orders for a given customer are processed sequentially. Also, the Orders topic is partitioned tor scalability.
Which factors would you need to address during application development?
- A. Ensure the message key is set to customer id, max-in-flight requests per connection is set to less than or equal to 5, and number of partitions is not changed.
- B. Enable producer Idempotence. set max-in-flight requests per connection to 1. and number of partitions is not changed.
- C. Enable producer Idempotence. set llnger.ms to 0. set acks='all\ and number of partitions is not changed.
- D. Enable producer Idempotence. set batch.size = 1. and set lioq.er.ms to 0. and number ot partitions is not changed.
Answer: A
NEW QUESTION # 19
How will you find out all the partitions without a leader?
- A. kafka-topics.sh --broker-list localhost:9092 --describe --under-replicated-partitions
- B. kafka-topics.sh --bootstrap-server localhost:2181 --describe --unavailable-partitions
- C. kafka-topics.sh --zookeeper localhost:2181 --describe --unavailable-partitions
- D. kafka-topics.sh --zookeeper localhost:2181 --describe --under-replicated-partitions
Answer: C
Explanation:
Please note that as of Kafka 2.2, the --zookeeper option is deprecated and you can now usekafka-topics.sh --bootstrap-server localhost:9092 --describe --unavailable-partitions
NEW QUESTION # 20
You need to explain the best reason to implement the consumer callback interface ConsumerRebalanceListener prior to a Consumer Group Rebalance.
Which statement is correct?
- A. Partition leaders may change.
- B. Previous log files are deleted.
- C. Offsets are compacted.
- D. Partitions assigned to a consumer may change.
Answer: D
Explanation:
The ConsumerRebalanceListener lets youhandle partition assignments and revocationsduring rebalances.
This is critical for managingoffsets,stateful processing, orexternal transactions.
FromKafka Consumer Rebalance Docs:
"Implementing ConsumerRebalanceListener allows your application to take action before and after partitions are reassigned."
* A is true: It lets your app react when partitionsassigned to the consumer change.
* B, C, and D are unrelated to consumer rebalancing directly.
Reference:Kafka Consumer JavaDocs > ConsumerRebalanceListener
NEW QUESTION # 21
You create a topic named stream-logs with:
* A replication factor of 3
* Four partitions
* Messages that are plain logs without a keyHow will messages be distributed across partitions?
- A. Messages will be distributed round-robin among all the topic partitions.
- B. Messages will be distributed among all the topic partitions with strict ordering.
- C. All messages will be written to the same log segment.
- D. The first message will always be written to partition 0.
Answer: A
Explanation:
If amessage key is not provided, Kafka's default partitioner usesround-robin distributionacross available partitions.
FromKafka Producer Design:
"If no key is provided, the default partitioner distributes messages round-robin to available partitions."
* A is incorrect - not all go to partition 0.
* C is invalid - Kafka doesn't group messages into one segment without a key.
* D is false - ordering isnot preservedacross partitions without a key.
Reference:Kafka Producer Partitioner Behavior
NEW QUESTION # 22
You need to correctly join data from two Kafka topics.
Which two scenarios will allow for co-partitioning?
(Select two.)
- A. Both topics have the same number of partitions.
- B. Both topics have the same key and partitioning strategy.
- C. Both topics have the same value schema.
- D. Both topics have the same retention time.
Answer: A,B
Explanation:
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.
Reference:Kafka Streams Concepts > Joins and Co-Partitioning
NEW QUESTION # 23
In Kafka, every broker... (select three)
- A. is a controller
- B. contains only a subset of the topics and the partitions
- C. knows the metadata for the topics and partitions it has on its disk
- D. knows all the metadata for all topics and partitions
- E. is a bootstrap broker
- F. contains all the topics and all the partitions
Answer: B,D,E
Explanation:
Kafka topics are divided into partitions and spread across brokers. Each brokers knows about all the metadata and each broker is a bootstrap broker, but only one of them is elected controller
NEW QUESTION # 24
You have a Kafka consumer in production actively reading from a critical topic.
You need to update the offset of your consumer to start reading from the beginning of the topic.
Which action should you take?
- A. Start a new consumer application with the same consumer group id.
- B. Temporarily configure the topic's retention.ms parameter to 0 to empty the topic.
- C. Update the consumer configuration by setting auto.offset.reset=earliest.
- D. Update the consumer group's offset to the earliest position using the kafka-consumer-groups CLI tool.
Answer: D
Explanation:
To reset offsets for anexisting consumer group, you must use thekafka-consumer-groups.shtool with the -- reset-offsets and --to-earliest flags.
FromKafka Consumer Group Tool Documentation:
"You can use the kafka-consumer-groups tool to reset offsets for a consumer group. This is required if the consumer has already committed offsets."
* Setting auto.offset.reset=earliest only worksif no committed offset exists.
* Starting a new consumer with the same group won't reset offsets.
* Retention settings don't affect committed offsets.
Reference:Kafka Consumer Group CLI Tool
NEW QUESTION # 25
A consumer application is using KafkaAvroDeserializer to deserialize Avro messages. What happens if message schema is not present in AvroDeserializer local cache?
- A. Fails silently
- B. Fetches schema from Schema Registry
- C. Throws DeserializationException
- D. Throws SerializationException
Answer: B
Explanation:
First local cache is checked for the message schema. In case of cache miss, schema is pulled from the schema registry. An exception will be thrown in the Schema Registry does not have the schema (which should never happen if you set it up properly)
NEW QUESTION # 26
(You are developing a Kafka Streams application with a complex topology that has multiple sources, processors, sinks, and sub-topologies.
You are working in a development environment and do not have access to a real Kafka cluster or topics.
You need to perform unit testing on your Kafka Streams application.
Which should you use?)
- A. KafkaUnitTestDriver
- B. MockProducer, MockConsumer
- C. TestProducer, TestConsumer
- D. TopologyTestDriver
Answer: D
Explanation:
The official Apache Kafka Streams developer documentation clearly states that TopologyTestDriver is the recommended and supported tool for unit testing Kafka Streams applications without requiring a running Kafka cluster. It is specifically designed to test complex topologies consisting of multiple sources, processors, state stores, sinks, and even sub-topologies.
TopologyTestDriver allows developers to instantiate the Kafka Streams topology directly in memory and feed test records into input topics while reading results from output topics. This makes it ideal for fast, deterministic, and isolated unit tests. As documented, it supports testing stateful and stateless operations, windowed aggregations, joins, and exactly-once behavior at the topology level.
Option A is incorrect because TestProducer and TestConsumer are not official Kafka Streams testing utilities.
Option B is invalid because KafkaUnitTestDriver does not exist in Apache Kafka. Option D (MockProducer and MockConsumer) belongs to the Kafka Clients API and is suitable for producer/consumer testing, but it does not support Kafka Streams topologies or state stores.
Therefore, TopologyTestDriver is the only fully supported and officially documented solution for unit testing Kafka Streams applications without a real Kafka cluster.
NEW QUESTION # 27
You are composing a REST request to create a new connector in a running Connect cluster. You invoke POST /connectors with a configuration and receive a 409 (Conflict) response.
What are two reasons for this response? (Select two.)
- A. The connector configuration was invalid, and the response body will expand on the configuration error.
- B. The connect cluster has reached capacity, and new connectors cannot be created without expandingthe cluster.
- C. The Connector already exists in the cluster.
- D. The Connect cluster is in process of rebalancing.
Answer: C,D
Explanation:
A409 Conflictresponse in Kafka Connect REST API usually means:
* The connector name provided already exists (i.e., a connector with that name is already deployed).
* The Kafka Connect cluster is temporarily unavailable to accept new tasks due toa rebalance in progress.
From theConfluent Developer documentationand Kafka Connect REST API spec:
"409 - Conflict. The connector name is already in use or the worker group is in the process of rebalancing and cannot complete the request at this time." Option A is incorrect because invalid configurations return400 Bad Request, not 409.
Option B is invalid because Kafka Connect does not have a strict concept of "capacity" that prevents connector creation with a 409.
Reference:Apache Kafka Connect REST API documentation
NEW QUESTION # 28
To allow consumers in a group to resume at the previously committed offset, I need to set the proper value for...
- A. enable.auto.commit
- B. auto.offset.resets
- C. value.deserializer
- D. group.id
Answer: D
Explanation:
Setting a group.id that's consistent across restarts will allow your consumers part of the same group to resume reading from where offsets were last committed for that group
NEW QUESTION # 29
In Avro, adding an element to an enum without a default is a __ schema evolution
- A. backward
- B. breaking
- C. full
- D. forward
Answer: B
Explanation:
Since Confluent 5.4.0, Avro 1.9.1 is used. Since default value was added to enum complex type , the schema resolution changed from:
(<1.9.1) if both are enums:** if the writer's symbol is not present in the reader's enum, then an error is signalled. **(>=1.9.1) if both are enums:
if the writer's symbol is not present in the reader's enum and the reader has a default value, then that value is used, otherwise an error is signalled.
NEW QUESTION # 30
Your application is consuming from a topic with one consumer group.
The number of running consumers is equal to the number of partitions.
Application logs show that some consumers are leaving the consumer group during peak time, triggering a rebalance. You also notice that your application is processing many duplicates.
You need to stop consumers from leaving the consumer group.
What should you do?
- A. Split consumers in different consumer groups.
- B. Increase session.timeout.ms property.
- C. Reduce max.poll.records property.
- D. Add more consumer instances.
Answer: B
Explanation:
If the consumer fails to send heartbeats in time (due to being slow under load), it is considereddead, causing a rebalance. Increasing session.timeout.ms gives the consumermore time to process messages and send heartbeats, preventing premature removal.
FromKafka Docs > Heartbeats and Failures:
"If the consumer does not send a heartbeat in session.timeout.ms, it is considered dead and the group coordinator will trigger a rebalance." Reducing max.poll.records mightslow consumption further. More consumers won't help since each partition already has a dedicated consumer.
Reference:Apache Kafka Consumer Configs > session.timeout.ms
NEW QUESTION # 31
You have a Kafka Connect cluster with multiple connectors.
One connector is not working as expected.
How can you find logs related to that specific connector?
- A. Modify the log4j.properties file to add a dedicated log appender for the connector.
- B. Change the log level to DEBUG to have connector context information in logs.
- C. Make no change, there is no way to find logs other than by stopping all the other connectors.
- D. Modify the log4j.properties file to enable connector context.
Answer: A
Explanation:
To isolate logs for a specific connector, you can configure a separate logger and appender in the Connect worker's log4j.properties file, using the connector's name as the logging context.
From Kafka Connect Logging Docs:
"Kafka Connect loggers use hierarchical logger names. You can configure per-connector log levels and output files by extending log4j.properties." A and C change verbosity but don't separate logs.
D is false; targeted logging is possible.
Reference: Kafka Connect > Logging and Debugging
NEW QUESTION # 32
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