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Confluent Certified Developer for Apache Kafka Certification Examination 認定 CCDAK 試験問題 (Q13-Q18):

質問 # 13
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?

A. Use Single Message Transforms to add schema and payload fields in the message
B. Set key.converter.schemas.enable and value.converter.schemas.enable to false
C. Set key.converter, value.converter to AvroConverter and the schema registry url
D. Set key.converter, value.converter to JsonConverter and the schema registry url

正解：B

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

質問 # 14
(A stream processing application tracks user activity in online shopping carts, including items added, removed, and ordered throughout the day for each user.
You need to capture data to identify possible periods of user inactivity.
Which type of Kafka Streams window should you use?)

A. Session
B. Tumbling
C. Sliding
D. Hopping

正解：A

解説：
According to the official Apache Kafka Streams documentation, session windows are specifically designed to model periods of activity separated by gaps of inactivity. A session window groups events that occur close together in time and closes when no new events arrive within a defined inactivity gap.
In this use case, tracking user shopping cart behavior requires detecting when a user becomes inactive (for example, stops adding or removing items). Session windows naturally represent this pattern by creating a new window when activity resumes after a period of silence. This makes them ideal for modeling user sessions, cart abandonment detection, and inactivity analysis.
Hopping and tumbling windows (Options B and C) are fixed-time windows and are not well-suited for detecting inactivity, as they segment data based on clock time rather than behavior. Sliding windows are primarily used for continuous joins and do not explicitly model inactivity gaps.
Therefore, session windows are the correct and officially recommended choice for identifying periods of user inactivity in Kafka Streams applications.

質問 # 15
Which statement is true about how exactly-once semantics (EOS) work in Kafka Streams?

A. Kafka Streams disables log compaction on internal changelog topics to preserve all state changes for potential recovery.
B. Kafka Streams provides EOS by periodically checkpointing state stores and replaying changelogs to recover only unprocessed messages during failure.
C. EOS in Kafka Streams is implemented by creating a separate Kafka topic for deduplication of all messages processed by the application.
D. EOS in Kafka Streams relies on transactional producers to atomically commit state updates to changelog topics and output records to Kafka.

正解：D

解説：
Kafka Streams uses transactional producers to guarantee exactly-once semantics (EOS). This ensures that both the output records and state store updates are committed atomically, avoiding duplication or partial writes.
From Kafka Streams Documentation > Processing Guarantees:
"Kafka Streams leverages Kafka's transactional APIs to commit the output records and internal state updates as a single atomic unit, thereby providing exactly-once semantics." Option A is incorrect because log compaction is not disabled for EOS.
Option C incorrectly describes a checkpointing system Kafka Streams does not use.
Option D refers to deduplication, which is not how EOS is achieved in Streams.
Reference: Kafka Streams Processing Guarantees

質問 # 16
What is accomplished by producing data to a topic with a message key?

A. Messages with the same key are routed to a deterministically selected partition, enabling order guarantees within that partition.
B. Consumers can filter messages in real time based on the message key without processing unrelated messages.
C. Kafka brokers allow you to add more partitions to a given topic, without impacting the data flow for existing keys.
D. It provides a mechanism for encrypting messages at the partition level to ensure secure data transmission.

正解：A

解説：
When amessage keyis specified in Kafka, the producer uses apartitioner(typically the default hash-based partitioner) todeterministically map all records with the same key to the same partition. Kafka guarantees order within a partition, so this enables per-key ordering.
FromKafka Producer Concepts:
"If a key is present, the producer will always route records with the same key to the same partition. Kafka preserves the order of records within a partition." This is essential for ordered processing and join semantics.
Reference:Kafka Producer Design > Partitions and Keys

質問 # 17
Which feature determines the maximum parallelism at which a Kafka Streams application can run?

A. Partitions of the input topic(s)
B. Configured Kafka Streams application instances
C. Brokers in the Kafka cluster
D. Input topics

正解：A

質問 # 18
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