Confluent CCDAK 100% Accuracy, Authentic CCDAK Exam Questions

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Confluent CCDAK Exam Syllabus Topics:

Section	Weight	Objectives
Developing with Kafka Consumers	15-20%	- Multi-threaded Consumers - Consumer Interceptors - Consumer Configuration and Tuning - Deserialization - Commit Strategies (Auto, Manual, Cooperative) - Consumer Groups and Rebalancing - Offset Management
Developing with Kafka Producers	15-20%	- Error Handling and Retry Logic - Partitioning Strategies - Idempotent Producers - Producer Interceptors - Producer Configuration and Tuning - Serialization (JSON, Avro, Protobuf) - Exactly-Once Semantics
Kafka Streams	20-25%	- State Stores and Processors - Transformation Operations (map, filter, join, aggregate) - Testing Kafka Streams Applications - Exactly-Once Processing - Windowed Operations (Tumbling, Hopping, Session) - Interactive Queries - Streams Architecture and Topology
Installing and Configuring Kafka	5-10%	- Topic Creation and Configuration - ZooKeeper vs KRaft Mode - Broker Configuration Parameters - Partition Assignment Strategies
Kafka Fundamentals	10-15%	- Brokers and Clusters - Producer and Consumer Basics - Replication Factor and ISR - Topics, Partitions, and Offsets - Log Segments and Retention
Schema Management	5-10%	- Schema Registry Architecture - Schema Registration and Retrieval - Schema Evolution and Compatibility - Protobuf and JSON Schemas - Avro Schemas
Monitoring and Operations	5-10%	- Disaster Recovery - Cross-region Replication - Consumer Lag and Health Checks - Kafka Metrics and Monitoring - Log Compaction
Security	5-10%	- Authentication (SASL, SSL/TLS) - Securing Schema Registry - Authorization (ACLs) - Encryption in Transit
Kafka Connect	10-15%	- Connect Architecture (Workers, Connectors, Tasks) - Converters and Transforms - Single Message Transformations (SMT) - Connector Configuration - Offset Management in Connect - Source and Sink Connectors

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Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions (Q46-Q51):

NEW QUESTION # 46
If I supply the setting compression.type=snappy to my producer, what will happen? (select two)

A. The Producers have to compress the data
B. The Consumers have to compress the data
C. The Consumers have to de-compress the data
D. The Kafka brokers have to compress the data
E. The Kafka brokers have to de-compress the data

Answer: C

Explanation:
Kafka transfers data with zero copy and no transformation. Any transformation (including compression) is the responsibility of clients.

NEW QUESTION # 47
How do you read a table or stream from the beginning of a topic?

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

Answer: B

NEW QUESTION # 48
How will you find out all the partitions without a leader?

A. kafka-topics.sh --bootstrap-server localhost:2181 --describe --unavailable-partitions
B. kafka-topics.sh --zookeeper localhost:2181 --describe --unavailable-partitions
C. kafka-topics.sh --broker-list localhost:9092 --describe --under-replicated-partitions
D. kafka-topics.sh --zookeeper localhost:2181 --describe --under-replicated-partitions

Answer: B

Explanation:
Please note that as of Kafka 2.2, the --zookeeper option is deprecated and you can now usekafka-topics.sh --bootstrap-server localhost:9092 --describe --unavailable-partitions

NEW QUESTION # 49
(You need to send a JSON message on the wire. The message key is a string.
How would you do this?)

A. Specify a value serializer class for the JSON contents of the message's value. Set a key serializer for the string value.
B. Specify a key serializer class for the JSON contents of the message's value. Set the value serializer class to null.
C. Specify a value serializer class for the JSON contents of the message's value. Set the key serializer class to null.
D. Specify a value serializer class for the JSON contents of the message's value. Set the key serializer class to JSON.

Answer: A

Explanation:
According to the Apache Kafka producer documentation, serialization is configured independently for message keys and values. The key and value can use different serializers depending on their data types.
In this scenario, the message key is a string, so the producer must be configured with org.apache.kafka.
common.serialization.StringSerializer for the key. The value contains JSON, so a suitable JSON serializer must be configured for the value, such as a custom JSON serializer, a library-based serializer, or a byte-array serializer after converting the JSON to bytes.
Option B correctly reflects this separation by specifying a string serializer for the key and a JSON serializer for the value. Kafka does not automatically infer serializers, and setting a serializer to null is invalid.
Additionally, there is no built-in "JSON" serializer for keys, making Option D incorrect.
Therefore, the correct approach-fully aligned with Kafka's producer configuration model-is to explicitly configure a StringSerializer for the key and a JSON-capable serializer for the value.

NEW QUESTION # 50
(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?)

A. TombstoneHandler
B. InsertField
C. HeaderFrom
D. RegexRouter

Answer: D

Explanation:
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.

NEW QUESTION # 51
......

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