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Confluent CCDAK Exam Syllabus Topics:
| Section | Weight | Objectives |
| Developing with Kafka Consumers | 15-20% | - Offset Management
- Commit Strategies (Auto, Manual, Cooperative)
- Consumer Groups and Rebalancing
- Consumer Interceptors
- Multi-threaded Consumers
- Deserialization
- Consumer Configuration and Tuning
|
| Installing and Configuring Kafka | 5-10% | - Broker Configuration Parameters
- ZooKeeper vs KRaft Mode
- Topic Creation and Configuration
- Partition Assignment Strategies
|
| Kafka Connect | 10-15% | - Connector Configuration
- Single Message Transformations (SMT)
- Offset Management in Connect
- Converters and Transforms
- Source and Sink Connectors
- Connect Architecture (Workers, Connectors, Tasks)
|
| Schema Management | 5-10% | - Schema Registry Architecture
- Avro Schemas
- Schema Evolution and Compatibility
- Schema Registration and Retrieval
- Protobuf and JSON Schemas
|
| Security | 5-10% | - Encryption in Transit
- Authorization (ACLs)
- Securing Schema Registry
- Authentication (SASL, SSL/TLS)
|
| Kafka Streams | 20-25% | - Streams Architecture and Topology
- Testing Kafka Streams Applications
- Interactive Queries
- Windowed Operations (Tumbling, Hopping, Session)
- Transformation Operations (map, filter, join, aggregate)
- State Stores and Processors
- Exactly-Once Processing
|
| Developing with Kafka Producers | 15-20% | - Producer Configuration and Tuning
- Exactly-Once Semantics
- Producer Interceptors
- Serialization (JSON, Avro, Protobuf)
- Error Handling and Retry Logic
- Partitioning Strategies
- Idempotent Producers
|
| Monitoring and Operations | 5-10% | - Kafka Metrics and Monitoring
- Cross-region Replication
- Log Compaction
- Consumer Lag and Health Checks
- Disaster Recovery
|
| Kafka Fundamentals | 10-15% | - Log Segments and Retention
- Producer and Consumer Basics
- Replication Factor and ISR
- Brokers and Clusters
- Topics, Partitions, and Offsets
|
Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions:
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.
Reveal Solution
Discussion
Correct Answer: A 🗳️
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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.)
- A. errors.deadletterqueue.context.headers.enable=true
- B. errors.log.enable=true
- C. errors.deadletterqueue.topic.name=DLQ-Topic
- D. errors.tolerance=none
- E. errors.tolerance=all
- F. errors.log.include.messages=true
Reveal Solution
Discussion
Correct Answer: A,C,E 🗳️
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(Your application consumes from a topic configured with a deserializer.
You want the application to be resilient to badly formatted records (poison pills).
You surround the poll() call with a try/catch block for RecordDeserializationException.
You need to log the bad record, skip it, and continue processing other records.
Which action should you take in the catch block?)
- A. Throw a runtime exception to trigger a restart of the application.
- B. Log the bad record and call consumer.skip() method.
- C. Log the bad record and seek the consumer to the offset of the next record.
- D. Log the bad record; no other action is needed.
Reveal Solution
Discussion
Correct Answer: C 🗳️
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Match the topic configuration setting with the reason the setting affects topic durability.
(You are given settings like unclean.leader.election.enable=false, replication.factor, min.insync.replicas=2)

Reveal Solution
Discussion
Correct Answer:

Explanation:
unclean.leader.election.enable=false # Prevents data loss by only considering in-sync replicas when rebalancing.
replication.factor # Specifies how many redundant copies of partitions are distributed across brokers.
min.insync.replicas=2 # Sets the standard for the number of partition instances that must keep up with the latest committed message.
unclean.leader.election.enable=false ensures that only in-sync replicas can be elected as leaders. If disabled, an out-of-sync replica may become leader, potentially leading to data loss.
replication.factor defines how many brokers will maintain copies of each partition, directly impacting durability and availability.
min.insync.replicas determines how many replicas must acknowledge a write when acks=all is used, enforcing write durability.
Reference: Apache Kafka Topic Configuration Documentation
The producer code below features a Callback class with a method called onCompletion().
In the onCompletion() method, when the request is completed successfully, what does the value metadata.
offset() represent?
- A. The ID of the partition to which the message was committed
- B. Its position in the producer's batch of messages
- C. The sequential ID of the message committed into a partition
- D. The number of bytes that overflowed beyond a producer batch of messages
Reveal Solution
Discussion
Correct Answer: C 🗳️
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