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Confluent CCDAK Exam Syllabus Topics:

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

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Confluent Certified Developer for Apache Kafka Certification Examination 認定 CCDAK 試験問題 (Q44-Q49):

質問 # 44
You create a producer that writes messages about bank account transactions from tens of thousands of different customers into a topic.
* Your consumers must process these messages with low latency and minimize consumer lag
* Processing takes ~6x longer than producing
* Transactions for each bank account must be processedin orderWhich strategy should you use?

A. Use a combination of the bank account number and the transaction timestamp as the message key.
B. Use a unique identifier such as a universally unique identifier (UUID) as the message key.
C. Use the timestamp of the message's arrival as its key.
D. Use the bank account number found in the message as the message key.

正解：D

解説：
To maintainmessage ordering per bank account, themessage key must be the account number. Kafka guaranteesordering per partition, and the partition is selected based on thehash of the key.
FromKafka Producer Docs:
"All records with the same key will be sent to the same partition, and Kafka preserves the order of records in a partition." Using account number ensures:
* All messages for the same account go to the same partition
* Order is maintained
* Workload is evenly distributed if account keys are varied
Other options break ordering or introduce unnecessary randomness.
Reference:Kafka Producer Concepts > Partitions and Keys

質問 # 45
A consumer wants to read messages from a specific partition of a topic. How can this be achieved?

A. Call assign() passing a Collection of TopicPartitions as the argument
B. Call subscribe(String topic, int partition) passing the topic and partition number as the arguments
C. Call subscribe() passing TopicPartition as the argument

正解：A

解説：
assign() can be used for manual assignment of a partition to a consumer, in which case subscribe() must not be used. Assign() takes a collection of TopicPartition object as an argument https://kafka.apache.org/23/javadoc/org/apache/kafka/clients/consumer/KafkaConsumer.html#assign-java.util.Collection-

質問 # 46
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. Messages will be distributed round-robin among all the topic partitions.
B. Messages will be distributed among all the topic partitions with strict ordering.
C. The first message will always be written to partition 0.
D. All messages will be written to the same log segment.

正解：A

解説：
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

質問 # 47
You have a consumer group with default configuration settings reading messages from your Kafka cluster.
You want to optimize its throughput so the consumer group processes more messages in the same amount of time.
Which changes increase the consumption throughput?

A. Disable auto commit and have the consumers manually commit offsets.
B. Remove some consumers from the consumer group.
C. Decrease the session timeout of each consumer.
D. Increase the number of bytes the consumers read with each fetch request.

正解：D

質問 # 48
Your application is consuming from a topic with one consumer group.
The number of running consumers is equal to the number of partitions.
Application logs show that some consumers are leaving the consumer group during peak time, triggering a rebalance. You also notice that your application is processing many duplicates.
You need to stop consumers from leaving the consumer group.
What should you do?

A. Split consumers in different consumer groups.
B. Increase session.timeout.ms property.
C. Reduce max.poll.records property.
D. Add more consumer instances.

正解：B

解説：
If the consumer fails to send heartbeats in time (due to being slow under load), it is considereddead, causing a rebalance. Increasing session.timeout.ms gives the consumermore time to process messages and send heartbeats, preventing premature removal.
FromKafka Docs > Heartbeats and Failures:
"If the consumer does not send a heartbeat in session.timeout.ms, it is considered dead and the group coordinator will trigger a rebalance." Reducing max.poll.records mightslow consumption further. More consumers won't help since each partition already has a dedicated consumer.
Reference:Apache Kafka Consumer Configs > session.timeout.ms

質問 # 49
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