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Confluent CCDAK Exam Syllabus Topics:

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

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Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions (Q48-Q53):

NEW QUESTION # 48
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. The first message will always be written to partition 0.
B. Messages will be distributed round-robin among all the topic partitions.
C. Messages will be distributed among all the topic partitions with strict ordering.
D. All messages will be written to the same log segment.

Answer: B

Explanation:
If a message key is not provided, Kafka's default partitioner uses round-robin distribution across available partitions.
From Kafka 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 is not preserved across partitions without a key.
Reference: Kafka Producer Partitioner Behavior

NEW QUESTION # 49
A stream processing application is tracking the user activity of online shopping carts. The application tracks items added to the cart, items removed from the cart, and items ordered from the cart throughout the day for each user.
You need to capture the data to identify possible periods of user inactivity.
Which type of Kafka Streams window should you use?

A. Session
B. Tumbling
C. Hopping
D. Sliding

Answer: A

NEW QUESTION # 50
(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=earliest and enable.auto.commit=falsewhile (true) {final var records = consumer.poll (POLL_TIMEOUT);consumer.commitAsync();for (var record : records) {// Any processing}}
B. 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();}
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: A

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 # 51
Which two producer exceptions are examples of the class RetriableException? (Select two.)

A. NotEnoughReplicasException
B. LeaderNotAvailableException
C. RecordTooLargeException
D. AuthorizationException

Answer: A,B

Explanation:
BothLeaderNotAvailableExceptionandNotEnoughReplicasExceptionare subclasses of RetriableException, which representstransient issuesthat may succeed upon retry.
FromApache Kafka Java Client Documentation:
"RetriableException indicates that the request can be retried. This includes network errors and certain broker- side failures like leader not available or not enough replicas."
* RecordTooLargeException and AuthorizationException arenon-retriablebecause they represent client misconfigurations or access issues.
Reference:Kafka Error Handling Guide > Retriable vs. Non-Retriable Exceptions

NEW QUESTION # 52
We have a store selling shoes. What dataset is a great candidate to be modeled as a KTable in Kafka Streams?

A. Inventory contents right now
B. Items returned
C. Money made until now
D. The transaction stream

Answer: B,C

Explanation:
Aggregations of stream are stored in table, whereas Streams must be modeled as a KStream to avoid data explosion

NEW QUESTION # 53
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