Updated PDF (New 2026) Actual Confluent CCDAK Exam Questions [Q51-Q65]

Published date August 24, 2026

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Updated PDF (New 2026) Actual Confluent CCDAK Exam Questions

Verified CCDAK Exam Dumps PDF [2026] Access using PassTestking

Confluent CCDAK Exam Syllabus Topics:

Section	Weight	Objectives
Apache Kafka Streams	12%	– Streams API concepts: topology, processors, state stores – Stream processing operations: map, filter, join, aggregation – Error handling and state management – Windowing and time semantics
Application Observability	13%	– Tracing and performance tuning – Logging and monitoring metrics – Error diagnosis and troubleshooting
Apache Kafka Fundamentals	23%	– Core concepts: brokers, topics, partitions, replicas, offsets – Delivery semantics: at-most-once, at-least-once, exactly-once – Kafka architecture and data flow – Configuration basics and cluster setup
Application Testing	8%	– Unit testing producers and consumers – Integration testing and test clusters – Mocking and test utilities
Apache Kafka Application Development	28%	– Consumer API: configuration, offset management, consumer groups, rebalancing – Transactional messaging and idempotence – Schema management: Confluent Schema Registry, compatibility rules, evolution – Producer API: configuration, serialization, partitioning, error handling
Kafka Connect	15%	– Source and sink connectors configuration – Data transformation and integration patterns – Connect architecture: workers, connectors, tasks, converters

QUESTION 51
(You are configuring a source connector that writes records to an Orders topic.
You need to send some of the records to a different topic.
Which Single Message Transform (SMT) is best suited for this requirement?)

RegexRouter

InsertField

TombstoneHandler

HeaderFrom

According to the official Apache Kafka Connect documentation, RegexRouter is the SMT specifically designed to dynamically change the destination topic name of records produced by a connector. It works by applying a regular expression to the original topic name and rewriting it to a new topic name.
This makes RegexRouter the correct choice when some records must be routed to a different topic, typically in combination with connector-level logic, predicates, or multiple connectors. It is commonly used for topic renaming, topic versioning, or routing records to alternate topics.
InsertField (Option B) only adds metadata fields (such as topic, partition, or timestamp) to the record payload and does not affect routing. TombstoneHandler (Option C) is used to manage null-value records, especially with compacted topics. HeaderFrom (Option D) copies fields into headers but does not change the target topic.
Therefore, RegexRouter is the only SMT that directly supports changing the output topic, as documented in the Kafka Connect SMT reference.

QUESTION 52
A consumer starts and has auto.offset.reset=none, and the topic partition currently has data for offsets going from 45 to 2311. The consumer group has committed the offset 10 for the topic before. Where will the consumer read from?

offset 45

offset 10

it will crash

offset 2311

auto.offset.reset=none means that the consumer will crash if the offsets it’s recovering from have been deleted from Kafka, which is the case here, as 10 < 45

QUESTION 53
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?

10 MB/s

0.33 MB/s

1 MB/s

5 MB/s

Each producer is allowed to produce @ 1MB/s to a broker. Max throughput 5 * 1MB, because we have 5 brokers.

QUESTION 54
To import data from external databases, I should use

Confluent REST Proxy

Kafka Connect Sink

Kafka Streams

Kafka Connect Source

Kafka Connect Sink is used to export data from Kafka to external databases and Kafka Connect Source is used to import from external databases into Kafka.

QUESTION 55
In Avro, removing or adding a field that has a default is a __ schema evolution

full

backward

breaking

forward

Clients with new schema will be able to read records saved with old schema and clients with old schema will be able to read records saved with new schema.

QUESTION 56
(What are two stateless operations in the Kafka Streams API?
Select two.)

Reduce

Join

Filter

GroupBy

In the Kafka Streams API, operations are classified as stateless or stateful based on whether they require maintaining local state stores. According to the official Kafka Streams documentation, stateless operations process each record independently, without storing or accessing prior records.
Filter is a stateless operation because it evaluates each record individually and decides whether to pass it downstream. It does not require any state or historical context.
GroupBy is also considered stateless because it merely repartitions the stream by assigning a new key and forwarding records to downstream processors. While it triggers the creation of an internal repartition topic, the GroupBy operation itself does not maintain a state store.
In contrast, Reduce is a stateful operation because it aggregates records over time and requires maintaining intermediate results in a state store. Similarly, Join operations are stateful because Kafka Streams must buffer and store records from one or both input streams or tables to perform the join within a defined time window.
Thus, the correct stateless operations are Filter and GroupBy, as documented in the Kafka Streams developer guide.

QUESTION 57
If I want to send binary data through the REST proxy to topic “test_binary”, it needs to be base64 encoded. A consumer connecting directly into the Kafka topic

“test_binary” will receive

binary data

avro data

json data

base64 encoded data, it will need to decode it

On the producer side, after receiving base64 data, the REST Proxy will convert it into bytes and then send that bytes payload to Kafka. Therefore consumers reading directly from Kafka will receive binary data.

QUESTION 58
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?

Enable producer Idempotence. set batch.size = 1. and set lioq.er.ms to 0. and number ot partitions is not changed.

Enable producer Idempotence. set max-in-flight requests per connection to 1. and number of partitions is not changed.

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.

Enable producer Idempotence. set llnger.ms to 0. set acks=’all and number of partitions is not changed.

QUESTION 59
When using plain JSON data with Connect, you see the following error messageorg.apache.kafka.connect.errors.DataExceptionJsonDeserializer with schemas.enable requires “schema” and “payload” fields and may not contain additional fields. How will you fix the error?

Set key.converter, value.converter to JsonConverter and the schema registry url

Use Single Message Transforms to add schema and payload fields in the message

Set key.converter.schemas.enable and value.converter.schemas.enable to false

Set key.converter, value.converter to AvroConverter and the schema registry url

You will need to set the schemas.enable parameters for the converter to false for plain text with no schema.

QUESTION 60
Your manager would like to have topic availability over consistency. Which setting do you need to change in order to enable that?

compression.type

unclean.leader.election.enable

min.insync.replicas

unclean.leader.election.enable=true allows non ISR replicas to become leader, ensuring availability but losing consistency as data loss will occur

QUESTION 61
Which of the following setting increases the chance of batching for a Kafka Producer?

Increase batch.size

Increase message.max.bytes

Increase the number of producer threads

Increase linger.ms

linger.ms forces the producer to wait to send messages, hence increasing the chance of creating batches

QUESTION 62
Compaction is enabled for a topic in Kafka by setting log.cleanup.policy=compact. What is true about log compaction?

After cleanup, only one message per key is retained with the first value

Each message stored in the topic is compressed

Kafka automatically de-duplicates incoming messages based on key hashes

After cleanup, only one message per key is retained with the latest value

Compaction changes the offset of messages
Explanation:
Log compaction retains at least the last known value for each record key for a single topic partition. All compacted log offsets remain valid, even if record at offset has been compacted away as a consumer will get the next highest offset.

QUESTION 63
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?

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

QUESTION 64
What is the protocol used by Kafka clients to securely connect to the Confluent REST Proxy?

Kerberos

SASL

HTTPS (SSL/TLS)

HTTP

TLS – but it is still called SSL.

QUESTION 65
Which statement is true about how exactly-once semantics (EOS) work in Kafka Streams?

Kafka Streams disables log compaction on internal changelog topics to preserve all state changes for potential recovery.

EOS in Kafka Streams relies on transactional producers to atomically commit state updates to changelog topics and output records to Kafka.

Kafka Streams provides EOS by periodically checkpointing state stores and replaying changelogs to recover only unprocessed messages during failure.

EOS in Kafka Streams is implemented by creating a separate Kafka topic for deduplication of all messages processed by the application.

Kafka Streams uses transactional producers to guarantee exactly-once semantics (EOS). This ensures that both the output records and state store updates are committed atomically, avoiding duplication or partial writes.
From Kafka Streams Documentation > Processing Guarantees:
“Kafka Streams leverages Kafka’s transactional APIs to commit the output records and internal state updates as 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.
Reference: Kafka Streams Processing Guarantees

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