Best Quality Confluent CCDAK Exam Questions TrainingQuiz Realistic Practice Exams [2025]
Critical Information To Confluent Certified Developer for Apache Kafka Certification Examination Pass the First Time
Confluent Certified Developer for Apache Kafka (CCDAK) Certification Exam is a globally recognized certification exam that validates the skills and knowledge of developers in building and managing Apache Kafka based solutions. Confluent Certified Developer for Apache Kafka Certification Examination certification exam is designed to test the candidate's understanding of the core concepts of Apache Kafka, including Kafka architecture, messaging patterns, and stream processing.
Confluent CCDAK certification exam is a rigorous examination that requires candidates to pass a series of comprehensive tests. Confluent Certified Developer for Apache Kafka Certification Examination certification is intended for developers who have a deep understanding of Kafka and have experience with it. CCDAK exam is designed to test the knowledge and skills of developers in Kafka and related technologies, and it is recognized globally as a standard for Kafka developers.
The CCDAK certification exam is designed to test the knowledge and skills of developers in building Kafka-based applications. CCDAK exam covers topics such as Kafka architecture, configuration, security, performance tuning, and application development using Kafka APIs. Confluent Certified Developer for Apache Kafka Certification Examination certification is vendor-neutral, meaning it is not tied to any particular distribution of Kafka, and is based on the most up-to-date version of Kafka available at the time of the exam.
NEW QUESTION # 19
Which two statements are correct about transactions in Kafka?
(Select two.)
- A. Information about producers and their transactions is stored in the _transaction_state topic.
- B. Transactions are only possible when writing messages to a topic with single partition.
- C. All messages from a failed transaction will be deleted from a Kafka topic.
- D. Consumers can consume both committed and uncommitted transactions.
- E. Transactions guarantee at least once delivery of messages.
Answer: A,D
Explanation:
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."
NEW QUESTION # 20
Which feature determines the maximum parallelism at which a Kafka Streams application can run?
- A. Input topics
- B. Configured Kafka Streams application instances
- C. Partitions of the input topic(s)
- D. Brokers in the Kafka cluster
Answer: C
NEW QUESTION # 21
The Controller is a broker that is... (select two)
- A. elected by broker majority
- B. elected by Zookeeper ensemble
- C. is responsible for consumer group rebalances
- D. is responsible for partition leader election
Answer: B,D
Explanation:
Controller is a broker that in addition to usual broker functions is responsible for partition leader election. The election of that broker happens thanks to Zookeeper and at any time only one broker can be a controller
NEW QUESTION # 22
Clients that connect to a Kafka cluster are required to specify one or more brokers in the 'bootstrap.servers' parameter.
What is the primary advantage to specifying more than one broker?
- A. It provides the ability to wake up dormant brokers.
- B. It provides redundancy in making the initial connection to the Kafka cluster.
- C. It is the mechanism to distribute a topic's partitions across multiple brokers.
- D. It forces clients to enumerate every single broker in the cluster.
Answer: B
NEW QUESTION # 23
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. Modify the log4j.properties file to enable connector context.
- C. Change the log level to DEBUG to have connector context information in logs.
- D. Make no change, there is no way to find logs other than by stopping all the other connectors.
Answer: A
Explanation:
To isolate logs for a specific connector, you can configurea separate logger and appenderin theConnect worker's log4j.propertiesfile, using the connector's name as the logging context.
FromKafka 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 # 24
How can you gracefully make a Kafka consumer to stop immediately polling data from Kafka and gracefully shut down a consumer application?
- A. Call consumer.wakeUp() and catch a WakeUpException
- B. Kill the consumer thread
- C. Call consumer.poll() in another thread
Answer: A
Explanation:
See https://stackoverflow.com/a/37748336/3019499
NEW QUESTION # 25
Producing with a key allows to...
- A. Ensure per-record level security
- B. Allow a Kafka Consumer to subscribe to a (topic,key) pair and only receive that data
- C. Add more information to my message
- D. Influence partitioning of the producer messages
Answer: D
Explanation:
Keys are necessary if you require strong ordering or grouping for messages that share the same key. If you require that messages with the same key are always seen in the correct order, attaching a key to messages will ensure messages with the same key always go to the same partition in a topic. Kafka guarantees order within a partition, but not across partitions in a topic, so alternatively not providing a key - which will result in round-robin distribution across partitions - will not maintain such order.
NEW QUESTION # 26
A producer just sent a message to the leader broker for a topic partition. The producer used acks=1 and therefore the data has not yet been replicated to followers. Under which conditions will the consumer see the message?
- A. When the message has been fully replicated to all replicas
- B. Never, the produce request will fail
- C. When the high watermark has advanced
- D. Right away
Answer: C
Explanation:
The high watermark is an advanced Kafka concept, and is advanced once all the ISR replicates the latest offsets. A consumer can only read up to the value of the High Watermark (which can be less than the highest offset, in the case of acks=1)
NEW QUESTION # 27
In Kafka, what are Topics split into?
- A. Sub Topics
- B. Chunks
- C. Partitions
- D. Consumers
Answer: C
NEW QUESTION # 28
Your application is consuming from a topic configured with a deserializer.
It needs to be resilient to badly formatted records ("poison pills"). You surround the poll() call with a try/catch for RecordDeserializationException.
You need to log the bad record, skip it, and continue processing.
Which action should you take in the catch block?
- A. Throw a runtime exception to trigger a restart of the application.
- B. Log the bad record and seek the consumer to the offset of the next record.
- C. Log the bad record, no other action needed.
- D. Log the bad record and call the consumer.skip() method.
Answer: B
Explanation:
To skip a corrupted record and avoid failing the application, you mustseek past the failed offsetmanually using consumer.seek(). This allows the application to resume consumption from the next offset.
FromKafka Consumer Error Handling Docs:
"On deserialization failure, you can catch RecordDeserializationException, log the error, and call seek() to the next offset to skip the bad record."
* A does not prevent re-processing the bad record.
* C is invalid; there'sno skip() methodin the Kafka consumer API.
* D results in service interruption - not ideal for resiliency.
Reference:Kafka Consumer API - Exception Handling and seek()
NEW QUESTION # 29
If a topic has a replication factor of 3...
- A. Each partition will live on 4 different brokers
- B. Each partition will live on 2 different brokers
- C. Each partition will live on 3 different brokers
- D. 3 replicas of the same data will live on 1 broker
Answer: C
Explanation:
Replicas are spread across available brokers, and each replica = one broker. RF 3 = 3 brokers
NEW QUESTION # 30
You have an existing topic t1 with four partitions.
Which statement is correct about changing the number of partitions for this topic?
- A. You may decrease the partition count if you change the partitioning algorithm.
- B. You may increase the partition count and Kafka will leave the existing data on the original partitions.
- C. You may increase the partition count and Kafka will ensure the message ordering remains the same.
- D. You may decrease the partition count if you increase the replication count.
Answer: B
NEW QUESTION # 31
What isn't an internal Kafka Connect topic?
- A. connect-status
- B. connect-jars
- C. connect-offsets
- D. connect-configs
Answer: B
Explanation:
connect-configs stores configurations, connect-status helps to elect leaders for connect, and connect-offsets store source offsets for source connectors
NEW QUESTION # 32
Consumer failed to process record # 10 and succeeded in processing record # 11. Select the course of action that you should choose to guarantee at least once processing
- A. Commit offsets at 11
- B. Commit offsets at 10
- C. Do not commit until successfully processing the record #10
Answer: A
Explanation:
Here, you shouldn't commit offsets 11 or 10 as it would indicate that the message #10 has been processed successfully.
NEW QUESTION # 33
How much should be the heap size of a broker in a production setup on a machine with 256 GB of RAM, in PLAINTEXT mode?
- A. 128 GB
- B. 16 GB
- C. 4 GB
- D. 512 MB
Answer: C
Explanation:
In Kafka, a small heap size is needed, while the rest of the RAM goes automatically to the page cache (managed by the OS). The heap size goes slightly up if you need to enable SSL
NEW QUESTION # 34
In the Kafka consumer metrics it is observed that fetch-rate is very high and each fetch is small. What steps will you take to increase throughput?
- A. Increase fetch.max.wait
- B. Increase fetch.max.bytes
- C. Decrease fetch.min.bytes
- D. Decrease fetch.max.bytes
- E. Increase fetch.min.bytes
Answer: E
Explanation:
This will allow consumers to wait and receive more bytes in each fetch request.
NEW QUESTION # 35
Match the testing tool with the type of test it is typically used to perform.
Answer:
Explanation:
Explanation:
* Unit Testing# MockProducer
* Integration Testing# Testcontainers
* Performance Testing# Trogdor
* Mock Data Generation# Connect Datagen
* MockProducer: Simulates a Kafka producer in unit tests (no real broker interaction).
* Testcontainers: Spawns Kafka in Docker forreal environment testing.
* Trogdor: Kafka's built-inperformance load testingframework.
* Connect Datagen: Createssample source recordsfor test and demo purposes.
FromKafka Developer Tools Guide:
"Kafka developers commonly use MockProducer for unit tests, Testcontainers for integration, and Trogdor for performance tests." Reference:Kafka Testing and Tools Overview
NEW QUESTION # 36
There are 3 brokers in the cluster. You want to create a topic with a single partition that is resilient to one broker failure and one broker maintenance. What is the replication factor will you specify while creating the topic?
- A. 0
- B. 1
- C. 2
- D. 3
Answer: A
Explanation:
1 is not possible as it doesn't provide resilience to failure, 2 is not enough as if we take a broker down for maintenance, we cannot tolerate a broker failure, and 6 is impossible as we only have 3 brokers (RF cannot be greater than the number of brokers). Here the correct answer is 3
NEW QUESTION # 37
You have a consumer group of 12 consumers and when a consumer gets killed by the process management system, rather abruptly, it does not trigger a graceful shutdown of your consumer. Therefore, it takes up to 10 seconds for a rebalance to happen. The business would like to have a 3 seconds rebalance time. What should you do? (select two)
- A. Increase heartbeat.interval.ms
- B. Decrease heartbeat.interval.ms
- C. Increase session.timeout.ms
- D. decrease max.poll.interval.ms
- E. increase max.poll.interval.ms
- F. Decrease session.timeout.ms
Answer: E,F
Explanation:
session.timeout.ms must be decreased to 3 seconds to allow for a faster rebalance, and the heartbeat thread must be quicker, so we also need to decrease heartbeat.interval.ms
NEW QUESTION # 38
How do you read a table or stream from the beginning of a topic?
- A. id as paymentId
FROM orders o
INNER JOIN payments p WITHIN 1 HOURS ON p.id = o.id
INNER JOIN shipments s WITHIN 2 HOURS ON s.id
= o.id; - B. id as shipmentId,
- C. itemId as itemId,
- D. SET 'auto.offset.reset' = 'earliest';
SELECT STRUCT(f1 := v1, f2 := v2) FROM s1 EMIT CHANGES; - E. CREATE STREAM shipped_orders AS
SELECT - F. SET 'auto.offset.reset' = 'latest';
SELECT STRUCT(f1 := v1, f2 := v2) FROM s1 EMIT CHANGES; - G. SELECT STRUCT(f1 := v1, f2 := v2) FROM s1 EMIT CHANGES;
- H. id as orderId
Answer: D
NEW QUESTION # 39
You need to collect logs from a host and write them to a Kafka topic named 'logs-topic'. You decide to use Kafka Connect File Source connector for this task.
What is the preferred deployment mode for this connector?
- A. Parallel mode
- B. SingleCluster mode
- C. Distributed mode
- D. Standalone mode
Answer: D
Explanation:
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.
Reference:Kafka Connect User Guide > Deployment Modes
NEW QUESTION # 40
There are five brokers in a cluster, a topic with 10 partitions and replication factor of 3, and a quota of producer_bytes_rate of 1 MB/sec has been specified for the client. What is the maximum throughput allowed for the client?
- A. 0.33 MB/s
- B. 5 MB/s
- C. 10 MB/s
- D. 1 MB/s
Answer: B
Explanation:
Each producer is allowed to produce @ 1MB/s to a broker. Max throughput 5 * 1MB, because we have 5 brokers.
NEW QUESTION # 41
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