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Confluent CCDAK certification exam is a rigorous examination that requires candidates to pass a series of comprehensive tests. Confluent Certified Developer for Apache Kafka Certification Examination certification is intended for developers who have a deep understanding of Kafka and have experience with it. CCDAK exam is designed to test the knowledge and skills of developers in Kafka and related technologies, and it is recognized globally as a standard for Kafka developers.
NEW QUESTION # 19
To enhance compression, I can increase the chances of batching by using
- A. acks=all
- B. max.message.size=10MB
- C. batch.size=65536
- D. linger.ms=20
Answer: D
Explanation:
linger.ms forces the producer to wait before sending messages, hence increasing the chance of creating batches that can be heavily compressed.
NEW QUESTION # 20
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)
Answer:
Explanation:
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
NEW QUESTION # 21
What are stateful operations in Kafka Streams API? (Choose 2.)
- A. joins
- B. map
- C. branch
- D. windowing
Answer: A,D
NEW QUESTION # 22
A consumer application runs once a week and reads from a Kafka topic. The last time the application ran, the last offset processed was 217. The application is configured with auto.offset.reset set to "latest". The current offsets in the topic start at 318 and end at 588.
What offset will the application start reading when it starts up for its next run?
- A. 0
- B. 1
- C. 2
- D. 3
Answer: B
NEW QUESTION # 23
A Kafka producer application wants to send log messages to a topic that does not include any key. What are the properties that are mandatory to configure for the producer configuration? (select three)
- A. bootstrap.servers
- B. key.serializer
- C. partition
- D. key
- E. value.serializer
- F. value
Answer: A,B,E
Explanation:
Both key and value serializer are mandatory.
NEW QUESTION # 24
You are sending messages with keys to a topic. To increase throughput, you decide to increase the number of partitions of the topic. Select all that apply.
- A. All the existing records will get rebalanced among the partitions to balance load
- B. New records with the same key will get written to the partition where old records with that key were written
- C. New records may get written to a different partition
- D. Old records will stay in their partitions
Answer: C,D
Explanation:
Increasing the number of partition causes new messages keys to get hashed differently, and breaks the guarantee "same keys goes to the same partition". Kafka logs are immutable and the previous messages are not re-shuffled
NEW QUESTION # 25
By default, which replica will be elected as a partition leader? (select two)
- A. Any of the replicas
- B. Preferred leader broker if it is in-sync and auto.leader.rebalance.enable=false
- C. An in-sync replica
- D. Preferred leader broker if it is in-sync and auto.leader.rebalance.enable=true
Answer: A,C
Explanation:
Preferred leader is a broker that was leader when topic was created. It is preferred because when partitions are first created, the leaders are balanced between brokers. Otherwise, any of the in-sync replicas (ISR) will be elected leader, as long as unclean.leader.election=false (by default)
NEW QUESTION # 26
A Zookeeper ensemble contains 5 servers. What is the maximum number of servers that can go missing and the ensemble still run?
- A. 0
- B. 1
- C. 2
- D. 3
Answer: B
Explanation:
majority consists of 3 zk nodes for 5 nodes zk cluster, so 2 can fail
NEW QUESTION # 27
You have a consumer group of 12 consumers and when a consumer gets killed by the process management system, rather abruptly, it does not trigger a graceful shutdown of your consumer. Therefore, it takes up to 10 seconds for a rebalance to happen. The business would like to have a 3 seconds rebalance time. What should you do? (select two)
- A. Decrease session.timeout.ms
- B. Decrease heartbeat.interval.ms
- C. increase max.poll.interval.ms
- D. Increase session.timeout.ms
- E. Increase heartbeat.interval.ms
- F. decrease max.poll.interval.ms
Answer: A,C
Explanation:
session.timeout.ms must be decreased to 3 seconds to allow for a faster rebalance, and the heartbeat thread must be quicker, so we also need to decrease heartbeat.interval.ms
NEW QUESTION # 28
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. The producer code has an expensive callback function.
- B. There is a bad data link layer (layer 2) connection from the producer to the cluster.
- C. The producer is sending large batches of messages.
- D. Compression is enabled.
Answer: A
Explanation:
Low I/O thread activity with blocked throughput often indicates that producer callbacks are consuming too much time, causing the sender thread to block while waiting for onCompletion() to finish.
From Kafka 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
NEW QUESTION # 29
The kafka-console-consumer CLI, when used with the default options
- A. always uses the same group id
- B. uses a random group id
- C. does not use a group id
Answer: B
Explanation:
If a group is not specified, the kafka-console-consumer generates a random consumer group.
NEW QUESTION # 30
Your configuration parameters for a Source connector and Connect worker are:
* offset.flush.interval.ms=60000
* offset.flush.timeout.ms=500
* offset.storage.topic=connect-offsets
* offset.storage.replication.factor=-1Which four statements match the expected behavior?(Select four.)
- A. The connector will commit offsets to a topic called connect-offsets.
- B. The offsets topic will use the broker default replication factor.
- C. The connector will wait 500ms for offset data to be committed.
- D. The connector will wait 60000ms before trying to commit offsets for tasks.
Answer: A,B,C,D
Explanation:
Each of these settings plays a key role inKafka Connect offset management:
* offset.flush.interval.ms=60000 # Offset commits happen every 60 seconds.
* offset.flush.timeout.ms=500 # The flush operation times out if it takes more than 500 ms.
* offset.storage.topic=connect-offsets # This is the internal topic for storing source offsets.
* offset.storage.replication.factor=-1 # This uses thebroker's default replication factor(commonly set via default.replication.factor).
FromKafka Connect Docs:
"If offset.storage.replication.factor=-1, the value will be determined by the broker's default replication factor." Reference:Kafka Connect Worker Configuration
NEW QUESTION # 31
In Kafka, what are Topics split into?
- A. Consumers
- B. Chunks
- C. Partitions
- D. Sub Topics
Answer: C
NEW QUESTION # 32
You are writing to a topic with acks=all.
The producer receives acknowledgments but you notice duplicate messages.
You find that timeouts due to network delay are causing resends.
Which configuration should you use to prevent duplicates?
- A. enable.auto.commit=true
- B. retries=2147483647
max.in.flight.requests.per.connection=5
enable.idempotence=true - C. retries=0
max.in.flight.requests.per.connection=5
enable.idempotence=true - D. retries=2147483647
max.in.flight.requests.per.connection=1
enable.idempotence=false
Answer: B
Explanation:
To ensureexactly-once deliveryand avoid duplicates even during retries:
* enable.idempotence=trueensures deduplication on the broker
* retries=2147483647allows unlimited retries on retriable errors
* max.in.flight.requests.per.connection=5is themaximum value that preserves message order with idempotence FromKafka Producer Config Docs:
"To achieve exactly-once semantics, set enable.idempotence=true, and max.in.flight.requests.per.connection #
5."
* A is unrelated (consumer-side)
* C disables retries
* D disables idempotence, leading to duplicates
Reference:Kafka Producer Configs > enable.idempotence, retries
NEW QUESTION # 33
Which statement describes the storage location for a sink connector's offsets?
- A. In a file specified by the offset.storage.file.filename configuration parameter
- B. The topic specified in the offsets.storage.topic configuration parameter
- C. The __consumer_offsets topic, like any other consumer
- D. In memory which is then periodically flushed to a RocksDB instance
Answer: C
Explanation:
Kafka Connect sink connectors use the standard Kafka consumer mechanism to track offsets, which means offsets are stored in the __consumer_offsets internal topic.
From Kafka Connect Documentation:
"Sink connectors are regular Kafka consumers and store their offsets in the __consumer_offsets topic like any other consumer." The other options refer to source connectors or worker configuration.
offsets.storage.topic is relevant for source connectors.
Reference: Kafka Connect Concepts > Sink Connectors and Offset Storage
NEW QUESTION # 34
In Avro, adding a field to a record without default is a __ schema evolution
- A. breaking
- B. forward
- C. full
- D. backward
Answer: B
Explanation:
Clients with old schema will be able to read records saved with new schema.
NEW QUESTION # 35
A consumer starts and has auto.offset.reset=none, and the topic partition currently has data for offsets going from 45 to 2311. The consumer group has committed the offset 10 for the topic before. Where will the consumer read from?
- A. offset 45
- B. offset 10
- C. offset 2311
- D. it will crash
Answer: D
Explanation:
auto.offset.reset=none means that the consumer will crash if the offsets it's recovering from have been deleted from Kafka, which is the case here, as 10 < 45
NEW QUESTION # 36
What data format isn't natively available with the Confluent REST Proxy?
- A. binary
- B. avro
- C. json
- D. protobuf
Answer: D
Explanation:
Protocol buffers isn't a natively supported type for the Confluent REST Proxy, but you may use the binary format instead
NEW QUESTION # 37
(You are building real-time streaming applications using Kafka Streams.
Your application has a custom transformation.
You need to define custom processors in Kafka Streams.
Which tool should you use?)
- A. Kafka Streams Custom Transformation Language
- B. Processor API
- C. Kafka Streams Domain Specific Language (DSL)
- D. TopologyTestDriver
Answer: B
Explanation:
The Apache Kafka Streams documentation clearly distinguishes between the Kafka Streams DSL and the Processor API. While the DSL is designed for common stream processing operations such as filtering, mapping, joining, and aggregations, it does not support fully custom processing logic.
For use cases that require custom transformations, fine-grained control over record processing, access to headers, timestamps, state stores, and punctuation, Kafka Streams provides the Processor API. This API allows developers to implement custom Processor, Transformer, or ValueTransformer classes and explicitly define the processing topology.
Option A (TopologyTestDriver) is a testing utility, not a development API. Option C (DSL) is higher-level and not suitable for advanced custom logic. Option D does not exist in Kafka.
Therefore, the correct and officially supported approach for defining custom processors in Kafka Streams is to use the Processor API.
NEW QUESTION # 38
Consumer failed to process record # 10 and succeeded in processing record # 11. Select the course of action that you should choose to guarantee at least once processing
- A. Commit offsets at 11
- B. Do not commit until successfully processing the record #10
- C. Commit offsets at 10
Answer: A
Explanation:
Here, you shouldn't commit offsets 11 or 10 as it would indicate that the message #10 has been processed successfully.
NEW QUESTION # 39
Select all the way for one consumer to subscribe simultaneously to the following topics - topic.history, topic.sports, topic.politics? (select two)
- A. consumer.subscribePrefix("topic.");
- B. consumer.subscribe(Pattern.compile("topic\..*"));
- C. consumer.subscribe("topic.history"); consumer.subscribe("topic.sports"); consumer.subscribe("topic.politics");
- D. consumer.subscribe(Arrays.asList("topic.history", "topic.sports", "topic.politics"));
Answer: B,D
Explanation:
Multiple topics can be passed as a list or regex pattern.
NEW QUESTION # 40
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The prominence of Kafka and streaming data has made the Confluent CCDAK Certification Exam an essential credential for IT professionals. The Confluent certification is an excellent way for IT professionals to demonstrate their skills and experience in Apache Kafka development in today's fast-changing technology ecosystem. Confluent Certified Developer for Apache Kafka Certification Examination certification also serves as a benchmark for employers looking to measure a candidate's expertise in Apache Kafka development before hiring them.
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