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Confluent CCDAK Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Kafka Connect	15%	- Source and sink connectors configuration - Data transformation and integration patterns - Connect architecture: workers, connectors, tasks, converters
Topic 2: Application Observability	13%	- Tracing and performance tuning - Logging and monitoring metrics - Error diagnosis and troubleshooting
Topic 3: Apache Kafka Application Development	28%	- Schema management: Confluent Schema Registry, compatibility rules, evolution - Producer API: configuration, serialization, partitioning, error handling - Transactional messaging and idempotence - Consumer API: configuration, offset management, consumer groups, rebalancing
Topic 4: Application Testing	8%	- Integration testing and test clusters - Unit testing producers and consumers - Mocking and test utilities
Topic 5: Apache Kafka Streams	12%	- Windowing and time semantics - Streams API concepts: topology, processors, state stores - Error handling and state management - Stream processing operations: map, filter, join, aggregation
Topic 6: Apache Kafka Fundamentals	23%	- Kafka architecture and data flow - Configuration basics and cluster setup - Delivery semantics: at-most-once, at-least-once, exactly-once - Core concepts: brokers, topics, partitions, replicas, offsets

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Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions (Q37-Q42):

NEW QUESTION # 37
To continuously export data from Kafka into a target database, I should use

A. Kafka Producer
B. Kafka Connect Source
C. Kafka Streams
D. Kafka Connect Sink

Answer: D

Explanation:
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.

NEW QUESTION # 38
Match each configuration parameter with the correct option.
To answer choose a match for each option from the drop-down. Partial
credit is given for each correct answer.

Answer:

Explanation:

Explanation:
Correct Matches
* path.format#Only valid for some Source Connectors
* value.converter#Valid for all Source Connectors
* input.path#Only valid for some Source Connectors
* errors.retry.timeout#Valid for all Source Connectors
* tasks.max#Valid for all Source Connectors
* path.formatThis is a connector-specific configuration, commonly used by file-based or storage-based source connectors (for example, HDFS, S3, or FilePulse connectors). It isnot part of the Kafka Connect framework itself, so it is only valid for some source connectors.
* value.converterThis is aworker-level configurationthat defines how record values are converted (JSON, Avro, Protobuf, etc.). It applies toall connectorsrunning on the worker unless overridden at the connector level.
* input.pathThis configuration is specific to file-based source connectors (e.g., FileStreamSourceConnector). It isnot universally supported, so it is only valid for some source connectors.
* errors.retry.timeoutThis is part of Kafka Connect'serror handling framework, introduced to support retries and tolerance of transient errors. It is ageneric Connect configurationand valid for all connectors.
* tasks.maxThis is amandatory, generic connector configurationthat controls the maximum number of tasks a connector may create. It is valid forall source (and sink) connectors.

NEW QUESTION # 39
How will you find out all the partitions without a leader?

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

Answer: C

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 # 40
(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. Plain Scaling
B. Vertical Scaling
C. Horizontal Scaling
D. Scaling Out

Answer: B

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 # 41
Your topic is log compacted and you are sending a message with the key K and value null. What will happen?

A. The broker will delete all messages with the key K upon cleanup
B. The broker will delete the message with the key K and null value only upon cleanup
C. The message will get ignored by the Kafka broker
D. The producer will throw a Runtime exception

Answer: A

Explanation:
Sending a message with the null value is called a tombstone in Kafka and will ensure the log compacted topic does not contain any messages with the key K upon compaction

NEW QUESTION # 42
......

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