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Confluent CCDAK Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Security	5-10%	- Encryption in Transit - Securing Schema Registry - Authorization (ACLs) - Authentication (SASL, SSL/TLS)
Topic 2: Kafka Fundamentals	10-15%	- Topics, Partitions, and Offsets - Brokers and Clusters - Producer and Consumer Basics - Replication Factor and ISR - Log Segments and Retention
Topic 3: Installing and Configuring Kafka	5-10%	- ZooKeeper vs KRaft Mode - Broker Configuration Parameters - Partition Assignment Strategies - Topic Creation and Configuration
Topic 4: Kafka Streams	20-25%	- Testing Kafka Streams Applications - Exactly-Once Processing - Windowed Operations (Tumbling, Hopping, Session) - State Stores and Processors - Transformation Operations (map, filter, join, aggregate) - Streams Architecture and Topology - Interactive Queries
Topic 5: Monitoring and Operations	5-10%	- Cross-region Replication - Disaster Recovery - Log Compaction - Kafka Metrics and Monitoring - Consumer Lag and Health Checks
Topic 6: Kafka Connect	10-15%	- Single Message Transformations (SMT) - Converters and Transforms - Connect Architecture (Workers, Connectors, Tasks) - Connector Configuration - Source and Sink Connectors - Offset Management in Connect
Topic 7: Developing with Kafka Consumers	15-20%	- Commit Strategies (Auto, Manual, Cooperative) - Consumer Interceptors - Consumer Groups and Rebalancing - Consumer Configuration and Tuning - Multi-threaded Consumers - Offset Management - Deserialization
Topic 8: Developing with Kafka Producers	15-20%	- Serialization (JSON, Avro, Protobuf) - Producer Configuration and Tuning - Error Handling and Retry Logic - Producer Interceptors - Idempotent Producers - Partitioning Strategies - Exactly-Once Semantics
Topic 9: Schema Management	5-10%	- Protobuf and JSON Schemas - Avro Schemas - Schema Registry Architecture - Schema Registration and Retrieval - Schema Evolution and Compatibility

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最新的 Confluent Certified Developer CCDAK 免費考試真題 (Q50-Q55):

問題 #50
What is the risk of increasing max.in.flight.requests.per.connection while also enabling retries in a producer?

A. Less resilient
B. Reduce throughput
C. Message order not preserved
D. At least once delivery is not guaranteed

答案：C

解題說明：
Some messages may require multiple retries. If there are more than 1 requests in flight, it may result in messages received out of order. Note an exception to this rule is if you enable the producer settingenable.idempotence=true which takes care of the out of ordering case on its own. Seehttps://issues.apache.org/jira/browse/KAFKA-5494

問題 #51
(You are experiencing low throughput from a Java producer.
Kafka producer metrics show a low I/O thread ratio and low I/O thread wait ratio.
What is the most likely cause of the slow producer performance?)

A. There is a bad data link layer (Layer 2) connection from the producer to the cluster.
B. Compression is enabled.
C. The producer code has an expensive callback function.
D. The producer is sending large batches of messages.

答案：C

解題說明：
According to the official Apache Kafka producer metrics documentation, the I/O thread ratio and I/O thread wait ratio indicate how busy the producer's network I/O thread is and how much time it spends waiting. When both metrics are low, it means the I/O thread is underutilized and not blocked on network operations.
This strongly suggests that the bottleneck is not network-related. If there were network latency or a bad data link (Option B), the I/O thread wait ratio would be high. Similarly, large batches (Option A) or compression (Option D) generally increase throughput and would result in higher I/O utilization.
An expensive or blocking callback function (for example, heavy logic in onCompletion()) executes in the producer's sender thread context. Kafka documentation explicitly warns that slow callbacks can throttle producer throughput because they delay the processing of acknowledgements and subsequent sends.
Therefore, the most likely and documented cause of low producer throughput with low I/O utilization is expensive callback logic in the producer code.

問題 #52
Your application is consuming from a topic configured with a deserializer.
It needs to be resilient to badly formatted records ("poison pills"). You surround the poll() call with a try/catch for RecordDeserializationException.
You need to log the bad record, skip it, and continue processing.
Which action should you take in the catch block?

A. Log the bad record, no other action needed.
B. Log the bad record and seek the consumer to the offset of the next record.
C. Throw a runtime exception to trigger a restart of the application.
D. Log the bad record and call the consumer.skip() method.

答案：B

解題說明：
To skip a corrupted record and avoid failing the application, you must seek past the failed offset manually using consumer.seek(). This allows the application to resume consumption from the next offset.
From Kafka Consumer Error Handling Docs:
"On deserialization failure, you can catch RecordDeserializationException, log the error, and call seek() to the next offset to skip the bad record." A does not prevent re-processing the bad record.
C is invalid; there's no skip() method in the Kafka consumer API.
D results in service interruption - not ideal for resiliency.
Reference: Kafka Consumer API - Exception Handling and seek()

問題 #53
A producer application in a developer machine was able to send messages to a Kafka topic. After copying the producer application into another developer's machine, the producer is able to connect to Kafka but unable to produce to the same Kafka topic because of an authorization issue. What is the likely issue?

A. The Kafka ACL does not allow another machine IP
B. You cannot copy a producer application from one machine to another
C. The Kafka Broker needs to be rebooted
D. Broker configuration needs to be changed to allow a different producer

答案：A

解題說明：
ACLs take "Host" as a parameter, which represents an IP. It can be * (all IP), or a specific IP. Here, it's a specific IP as moving a producer to a different machine breaks the consumer, so the ACL needs to be updated

問題 #54
You are experiencing low throughput from a Java producer.
Metrics show low I/O thread ratio and low I/O thread wait ratio.
What is the most likely cause of the slow producer performance?

A. Compression is enabled.
B. There is a bad data link layer (layer 2) connection from the producer to the cluster.
C. The producer code has an expensive callback function.
D. The producer is sending large batches of messages.

答案：C

解題說明：
Low I/O thread activity with blocked throughput often indicates thatproducer callbacks are consuming too much time, causing the sender thread to block while waiting for onCompletion() to finish.
FromKafka Producer Performance Guide:
"Expensive logic in callbacks (e.g., I/O or complex computation) can block the sender thread, reducing throughput."
* Compression (A) may slightly impact CPU but not I/O thread usage.
* Large batches (B) improve throughput if managed correctly.
* A Layer 2 network issue (C) would lead to packet loss, not specifically low callback metrics.
Reference:Kafka Producer Metrics and Performance Tuning

問題 #55
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