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Confluent CCDAK Exam Syllabus Topics:

SectionWeightObjectives
Apache Kafka Fundamentals23%- Delivery semantics: at-most-once, at-least-once, exactly-once
- Core concepts: brokers, topics, partitions, replicas, offsets
- Kafka architecture and data flow
- Configuration basics and cluster setup
Kafka Connect15%- Connect architecture: workers, connectors, tasks, converters
- Data transformation and integration patterns
- Source and sink connectors configuration
Apache Kafka Application Development28%- Transactional messaging and idempotence
- Producer API: configuration, serialization, partitioning, error handling
- Consumer API: configuration, offset management, consumer groups, rebalancing
- Schema management: Confluent Schema Registry, compatibility rules, evolution
Apache Kafka Streams12%- Windowing and time semantics
- Streams API concepts: topology, processors, state stores
- Stream processing operations: map, filter, join, aggregation
- Error handling and state management
Application Observability13%- Tracing and performance tuning
- Logging and monitoring metrics
- Error diagnosis and troubleshooting
Application Testing8%- Integration testing and test clusters
- Mocking and test utilities
- Unit testing producers and consumers

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Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions (Q44-Q49):

NEW QUESTION # 44
Your producer is producing at a very high rate and the batches are completely full each time. How can you improve the producer throughput? (select two)

Answer: C,D

Explanation:
Increase linger.ms
Explanation:
batch.size controls how many bytes of data to collect before sending messages to the Kafka broker. Set this as high as possible, without exceeding available memory. Enabling compression can also help make more compact batches and increase the throughput of your producer. Linger.ms will have no effect as the batches are already full


NEW QUESTION # 45
To read data from a topic, the following configuration is needed for the consumers

Answer: C

Explanation:
All brokers can respond to Metadata request, so a client can connect to any broker in the cluster.


NEW QUESTION # 46
A kafka topic has a replication factor of 3 and min.insync.replicas setting of 2. How many brokers can go down before a producer with acks=all can't produce?

Answer: A

Explanation:
acks=all and min.insync.replicas=2 means we must have at least 2 brokers up for the partition to be available


NEW QUESTION # 47
You need to configure a sink connector to write records that fail into a dead letter queue topic.
Requirements:
* Topic name: DLQ-Topic
* Headers containing error context must be added to the messagesWhich three configuration parameters are necessary?(Select three.)

Answer: D,E,F

Explanation:
To send failed records to adead letter queue (DLQ), you must configure:
* errors.tolerance=all: Tells the connector tonot failon errors but handle them (e.g., send to DLQ).
* errors.deadletterqueue.topic.name=DLQ-Topic: Specifies the DLQ topic.
* errors.deadletterqueue.context.headers.enable=true: Includes error context in message headers.
FromKafka Connect Error Handling Docs:
"Kafka Connect supports directing problematic records to a separate topic (DLQ) using errors.* configs.
Headers can include failure metadata."
Options D, E, F are related tologging, not DLQ behavior.
Reference:Kafka Connect Configurations > Error Handling


NEW QUESTION # 48
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?

Answer: C

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


NEW QUESTION # 49
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