[Q13-Q29] CCDAK Free Update With 100% Exam Passing Guarantee [2026]

admin CCDAK Confluent July 26, 2026

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CCDAK Free Update With 100% Exam Passing Guarantee [2026]

[Jul-2026] Verified Confluent Exam Dumps with CCDAK Exam Study Guide

The Confluent CCDAK exam covers various topics, including Kafka architecture, messaging patterns, configuration, security, and performance tuning. Developers who pass the exam will demonstrate their ability to develop and deploy high-performance, reliable, and scalable Kafka applications that meet the needs of modern data-driven businesses. Confluent Certified Developer for Apache Kafka Certification Examination certification is an excellent way for developers to showcase their expertise in Kafka and differentiate themselves in the job market.

NEW QUESTION 13
The exactly once guarantee in the Kafka Streams is for which flow of data?

Kafka => Kafka

Kafka => External

External => Kafka

Kafka Streams can only guarantee exactly once processing if you have a Kafka to Kafka topology.

NEW QUESTION 14
This schema excerpt is an example of which schema format?
package com.mycorp.mynamespace;
message SampleRecord {
int32 Stock = 1;
double Price = 2;
string Product_Name = 3;
}

Avro

Protobuf

JSON Schema

YAML

This syntax is a clear match to Protocol Buffers (Protobuf). It defines a schema with fields, types, and tags, which is a format supported by Kafka when using Protobuf-based schema registry serialization.
From Confluent Schema Registry Docs:
“Kafka supports Protobuf serialization, where schemas are written in .proto files and include fields with tags.” int32, double, and string are standard Protobuf types.
Avro uses JSON-style schema.
JSON Schema uses JSON object structure, not .proto.
YAML is unrelated.
Reference: Confluent Schema Registry for Protobuf

NEW QUESTION 15
What’s is true about Kafka brokers and clients from version 0.10.2 onwards?

Clients and brokers must have the exact same version to be able to communicate

A newer client can talk to a newer broker, but an older client cannot talk to a newer broker

A newer client can talk to a newer broker, and an older client can talk to a newer broker

A newer client can’t talk to a newer broker, but an older client can talk to a newer broker

Kafka’s new bidirectional client compatibility introduced in 0.10.2 allows this. Read more herehttps://www.
confluent.io/blog/upgrading-apache-kafka-clients-just-got-easier/

NEW QUESTION 16
A Zookeeper ensemble contains 5 servers. What is the maximum number of servers that can go missing and the ensemble still run?

majority consists of 3 zk nodes for 5 nodes zk cluster, so 2 can fail

NEW QUESTION 17
(You are implementing a Kafka Streams application to process financial transactions.
Each transaction must be processed exactly once to ensure accuracy.
The application reads from an input topic, performs computations, and writes results to an output topic.
During testing, you notice duplicate entries in the output topic, which violates the exactly-once processing requirement.
You need to ensure exactly-once semantics (EOS) for this Kafka Streams application.
Which step should you take?)

Enable compaction on the output topic to handle duplicates.

Set enable.idempotence=true in the internal producer configuration of the Kafka Streams application.

Set enable.exactly_once=true in the Kafka Streams configuration.

Set processing.guarantee=exactly_once_v2 in the Kafka Streams configuration.

According to the official Apache Kafka documentation for Kafka Streams, exactly-once semantics (EOS) are controlled using the processing.guarantee configuration. To ensure that each input record is processed once and only once, and that the results are written atomically to the output topic, Kafka Streams provides built-in EOS support.
The correct and recommended configuration is processing.guarantee=exactly_once_v2, which is the default EOS implementation starting from Kafka 3.0. This mode uses Kafka’s transactional APIs to atomically commit consumer offsets and produce output records within a single transaction. As documented, this prevents duplicates even in the presence of failures, retries, or application restarts.
Option A is incorrect because log compaction does not guarantee exactly-once processing; it only retains the latest record per key. Option B is insufficient because enable.idempotence alone does not handle consumer offset commits or state store consistency in Kafka Streams. Option C is invalid because no such configuration exists in Kafka Streams.
Therefore, setting processing.guarantee=exactly_once_v2 is the only fully correct and officially supported solution for achieving EOS in Kafka Streams applications.

NEW QUESTION 18
A producer application was sending messages to a partition with a replication factor of 2 by connecting to Broker 1 that was hosting partition leader. If the Broker 1 goes down, what will happen?

The producer will automatically produce to the broker that has been elected leader

The topic will be unavailable

The producer will stop working

Once the client connects to any broker, it is connected to the entire cluster and in case of leadership changes, the clients automatically do a Metadata Request to an available broker to find out who is the new leader for the topic. Hence the producer will automatically keep on producing to the correct Kafka Broker

NEW QUESTION 19
The rule “same key goes to the same partition” is true unless…

the number of producer changes

the number of kafka broker changes

the number of partition changes

the replication factor changes

Increasing the number of partition causes new messages keys to get hashed differently, and breaks the guarantee “same keys goes to the same partition”. Kafka logs are immutable and the previous messages are not re-shuffled.

NEW QUESTION 20
What is true about replicas ?

Produce requests can be done to the replicas that are followers

Produce and consume requests are load-balanced between Leader and Follower replicas

Leader replica handles all produce and consume requests

Follower replica handles all consume requests

Replicas are passive – they don’t handle produce or consume request. Produce and consume requests get sent to the node hosting partition leader.

NEW QUESTION 21
You have a Kafka client application that has real-time processing requirements.
What are the most important metrics available in Kafka that you should monitor?

Consumer lag between the brokers and the consumers

Total time to serve requests to replica followers

Consumer heartbeat rate to the group coordinator

Aggregate incoming byte rate

NEW QUESTION 22
Which configuration allows more time for the consumer poll to process records?

session.timeout.ms

heartbeat.interval.ms

max.poll.interval.ms

fetch.max.wait.ms

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.
Reference:Apache Kafka Consumer Configs

NEW QUESTION 23
The kafka-console-consumer CLI, when used with the default options

uses a random group id

always uses the same group id

does not use a group id

If a group is not specified, the kafka-console-consumer generates a random consumer group.

NEW QUESTION 24
(You create a topic with five partitions.
What can you assume about messages read from that topic by a single consumer group?)

Messages can be consumed by a maximum of five consumers in the same consumer group.

The consumer group can only read the same number of messages from all the partitions.

All messages will be read from exactly one broker by the consumer group.

Messages from one partition can be consumed by any of the consumers in a group for faster processing.

The Apache Kafka documentation explains that Kafka’s parallelism model is based on topic partitions and consumer groups. Within a single consumer group, each partition can be assigned to at most one consumer at a time. As a result, the maximum level of parallelism for a consumer group is equal to the number of partitions in the topic.
In this scenario, the topic has five partitions, which means that up to five consumers in the same consumer group can actively consume data in parallel. If more than five consumers are added, the extra consumers will remain idle because there are no additional partitions to assign.
Option B is incorrect because Kafka does not enforce equal message consumption across partitions; message distribution depends on producers and partitioning strategy. Option C is false because partitions may be hosted on different brokers, and consumers fetch data from multiple brokers. Option D is incorrect because a single partition cannot be consumed by multiple consumers within the same group simultaneously.
Therefore, the only valid assumption is that a maximum of five consumers can actively consume messages in the same consumer group.

NEW QUESTION 25
What is the risk of increasing max.in.flight.requests.per.connection while also enabling retries in a producer?

At least once delivery is not guaranteed

Message order not preserved

Reduce throughput

Less resilient

Some messages may require multiple retries. If there are more than 1 requests in flight, it may result in messages received out of order. Note an exception to this rule is if you enable the producer settingenable.idempotence=true which takes care of the out of ordering case on its own. Seehttps://issues.apache.org/jira/browse/KAFKA-5494

NEW QUESTION 26
Which of the following is not an Avro primitive type?

string

long

int

date

null

date is a logical type

NEW QUESTION 27
is KSQL ANSI SQL compliant?

Yes

KSQL is not ANSI SQL compliant, for now there are no defined standards on streaming SQL languages

NEW QUESTION 28
Refer to the producer code below. It features a ‘Callback’ class with a method called ‘onCompletion()’.
In the ‘on Completion*.)’ method, what does the ‘metadata.offset()’ value represent?

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

The sequential id of the message is committed into a partition

Its position in the producer’s batch of messages

The number of bytes that overflowed beyond a producer batch of messages

The id of the partition that the message was committed to

NEW QUESTION 29
An application is writing AVRO messages using Schema Registry to topic t1. During this process, the Schema Registry becomes unavailable for a few seconds.
What is the expected impact to the application?

The application may not have any impact, unless it is writing messages with a new Schema Definition.

All messages within that time will receive an error.

Since messages are cached by the producer, the application will only get an error if the producer is sending the batch at that time.

Since the broker will eventually replicate the message Schema, there will not be an error.

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Confluent CCDAK (Confluent Certified Developer for Apache Kafka Certification Examination) Certification Exam is a valuable certification for developers who work with Kafka. CCDAK exam tests the skills and knowledge of developers in various aspects of Kafka, and passing it demonstrates proficiency in working with Kafka. Candidates can prepare for the exam by gaining theoretical knowledge and practical experience, and there are many resources available to help them do so.

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