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Confluent CCDAK Exam Syllabus Topics:

Section	Objectives
Topic 1: Kafka Streams and Processing	- Kafka Streams API 1. Stream processing fundamentals 2. Stateful vs stateless processing - Event processing patterns 1. Windowing and aggregation 2. Exactly-once semantics
Topic 2: Kafka Fundamentals	- Kafka architecture and core concepts 1. Brokers, topics, partitions 2. Replication and fault tolerance - Data flow in Kafka 1. Offset management 2. Producer and consumer model
Topic 3: Kafka Producers and Consumers	- Producer API 1. Reliability and acknowledgments 2. Serialization and partitioning strategies - Consumer API 1. Consumer groups 2. Rebalancing and offset commits
Topic 4: Kafka Ecosystem and Confluent Platform	- ksqlDB and connectors 1. Kafka Connect basics 2. Stream querying concepts - Schema Registry 1. Avro, Protobuf, JSON Schema usage

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100% Pass 2026 Confluent Fantastic CCDAK: Confluent Certified Developer for Apache Kafka Certification Examination Regualer Update

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Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions (Q91-Q96):

NEW QUESTION # 91
(You create an Orders topic with 10 partitions.
The topic receives data at high velocity.
Your Kafka Streams application initially runs on a server with four CPU threads.
You move the application to another server with 10 CPU threads to improve performance.
What does this example describe?)

A. Horizontal Scaling
B. Plain Scaling
C. Scaling Out
D. Vertical Scaling

Answer: D

Explanation:
This scenario describes vertical scaling, as defined in the Apache Kafka Streams documentation and general distributed systems terminology. Vertical scaling refers to adding more resources (CPU, memory, or disk) to a single machine to increase processing capacity.
In this example, the Kafka Streams application is moved from a server with four CPU threads to one with ten CPU threads, increasing the available compute resources without increasing the number of application instances. Kafka Streams can take advantage of additional CPU threads through its num.stream.threads configuration, allowing more partitions to be processed in parallel within the same application instance.
Horizontal scaling (also known as scaling out) would involve adding more application instances across multiple servers, not upgrading a single server. Option D is therefore incorrect, and Option C is not a recognized scaling term.
Thus, this example clearly illustrates vertical scaling, where performance is improved by increasing the capacity of a single node.

NEW QUESTION # 92
What's a Kafka partition made of?

A. One file and one index
B. One file and two indexes per segment
C. One file and two indexes
D. One file

Answer: B

Explanation:
Kafka partitions are made of segments (usually each segment is 1GB), and each segment has two corresponding indexes (offset index and time index)

NEW QUESTION # 93
How do you read a table or stream from the beginning of a topic?

A. id as shipmentId,
B. SET 'auto.offset.reset' = 'earliest';
SELECT STRUCT(f1 := v1, f2 := v2) FROM s1 EMIT CHANGES;
C. itemId as itemId,
D. id as orderId
E. SET 'auto.offset.reset' = 'latest';
SELECT STRUCT(f1 := v1, f2 := v2) FROM s1 EMIT CHANGES;
F. 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;
G. SELECT STRUCT(f1 := v1, f2 := v2) FROM s1 EMIT CHANGES;
H. CREATE STREAM shipped_orders AS
SELECT

Answer: B

NEW QUESTION # 94
StreamsBuilder builder = new StreamsBuilder();
KStream<String, String> textLines = builder.stream("word-count-input"); KTable<String, Long> wordCounts = textLines
.mapValues(textLine -> textLine.toLowerCase())
.flatMapValues(textLine -> Arrays.asList(textLine.split("\W+")))
.selectKey((key, word) -> word)
.groupByKey()
.count(Materialized.as("Counts"));
wordCounts.toStream().to("word-count-output", Produced.with(Serdes.String(), Serdes.Long())); builder.build(); What is an adequate topic configuration for the topic word-count-output?

A. max.message.bytes=10000000
B. cleanup.policy=compact
C. cleanup.policy=delete
D. compression.type=lz4

Answer: B

Explanation:
Result is aggregated into a table with key as the unique word and value its frequency. We have to enable log compaction for this topic to align the topic's cleanup policy with KTable semantics.

NEW QUESTION # 95
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 forces clients to enumerate every single broker in the cluster.
B. It is the mechanism to distribute a topic's partitions across multiple brokers.
C. It provides the ability to wake up dormant brokers.
D. It provides redundancy in making the initial connection to the Kafka cluster.

Answer: D

NEW QUESTION # 96
......

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