Latest [Apr 09, 2026] 100% Passing Guarantee - Brilliant CCDAK Exam Questions PDF
CCDAK Certification – Valid Exam Dumps Questions Study Guide! (Updated 92 Questions)
Confluent CCDAK (Confluent Certified Developer for Apache Kafka) Certification Exam is a globally recognized certification exam that tests the expertise of developers in Apache Kafka. Apache Kafka is an open-source distributed event streaming platform that is widely used by companies for building real-time data pipelines and streaming applications. The CCDAK exam is designed to test the knowledge and skills of developers in building and managing Kafka-based applications.
NEW QUESTION # 47
Your Kafka cluster has five brokers. The topic t1 on the cluster has two partitions, and it has replication.factor
= 4, min.insync.replicas = 3.
You want to have strong durability guarantees for messages written to topic t1.
If you configure a producer 'acks=all', how many brokers need to acknowledge a message before it is considered committed?
- A. 0
- B. 1
- C. 2
- D. 3
Answer: D
NEW QUESTION # 48
Which is true about topic compaction?
- A. Topic compaction does not remove old events; instead, when clients consume events from a compacted topic, they store events in a hashmap that maintains the latest value.
- B. Compaction will keep exactly one message per key after compaction of inactive log segments.
- C. When a client produces a new event with an existing key, the old value is overwritten with the new value in the compacted log segment.
- D. When a client produces a new event with an existing key, the broker immediately deletes the offset of the existing event.
Answer: B
Explanation:
Log compactionensures that Kafka retains at least thelatest value per keyin a topic. Compaction happensin the backgroundand removes older records with the same keyin inactive log segments, not immediately.
From theKafka Documentation > Log Compaction:
"Kafka guarantees that thelast message for each keywill be retained in the log after compaction, even if earlier messages with the same key are deleted." So, D is correct. A is incorrect because compaction does not overwrite; it's a background process. B is incorrect-deletion is not immediate. C incorrectly suggests client-side hashmap behavior.
Reference:Apache Kafka Log Compaction Docs
NEW QUESTION # 49
You use Kafka Connect with the JDBC source connector to extract data from a large database and push it into Kafka.
The database contains tens of tables, and the current connector is unable to process the data fast enough.
You add more Kafka Connect workers, but throughput doesn't improve.
What should you do next?
- A. Increase the number of Kafka partitions for the topics.
- B. Modify the database schemas to enable horizontal sharding.
- C. Add more Kafka brokers to the cluster.
- D. Increase the value of the connector's property tasks.max.
Answer: D
Explanation:
Increasing tasks.max allows the connector to create multiple tasks, each responsible for processing different tables or table partitions. If only one task is used, adding more workers has no effect.
From Kafka Connect JDBC Connector Docs:
"Use tasks.max to configure how many tasks are created for the connector. Each task can process a subset of the data." A and C help only if the bottleneck is in Kafka, not Connect.
D is a large architectural change, not the first step.
Reference: Kafka Connect JDBC Connector Configuration
NEW QUESTION # 50
Your Kafka cluster has five brokers. The topic t1 on the cluster has:
* Two partitions
* Replication factor = 4
* min.insync.replicas = 3You need strong durability guarantees for messages written to topic t1.You configure a producer acks=all and all the replicas for t1 are in-sync.How many brokers need to acknowledge a message before it is considered committed?
- A. 0
- B. 1
- C. 2
- D. 3
Answer: C
Explanation:
With acks=all, the leader waits formin.insync.replicasto acknowledge the message. Since min.insync.
replicas=3, Kafka will only commit the messageonce 3 brokers (leader + 2 followers)confirm they have the message.
FromKafka Documentation > Acks and Durability:
"If acks=all is specified, the producer will wait until the full set of in-sync replicas has acknowledged the record. The minimum number of in-sync replicas is controlled by min.insync.replicas." Even though the replication factor is 4, only3 acknowledgments are needed, as defined by min.insync.
replicas.
Reference:Apache Kafka Producer Configs > acks, min.insync.replicas
NEW QUESTION # 51
An ecommerce website maintains two topics - a high volume "purchase" topic with 5 partitions and low volume "customer" topic with 3 partitions. You would like to do a stream-table join of these topics. How should you proceed?
- A. Do a KStream / KTable join after a repartition step
- B. Model customer as a GlobalKTable
- C. Repartition customer topic to have 5 partitions
- D. Repartition the purchase topic to have 3 partitions
Answer: B
Explanation:
In case of KStream-KStream join, both need to be co-partitioned. This restriction is not applicable in case of join with GlobalKTable, which is the most efficient here.
NEW QUESTION # 52
Which feature determines the maximum parallelism at which a Kafka Streams application can run?
- A. Brokers in the Kafka cluster
- B. Configured Kafka Streams application instances
- C. Partitions of the input topic(s)
- D. Input topics
Answer: C
NEW QUESTION # 53
A consumer is configured with enable.auto.commit=false. What happens when close() is called on the consumer object?
- A. The group coordinator will discover that the consumer stopped sending heartbeats. It will cause rebalance after session.timeout.ms
- B. The uncommitted offsets are committed
- C. A rebalance in the consumer group will happen immediately
Answer: C
Explanation:
Calling close() on consumer immediately triggers a partition rebalance as the consumer will not be available anymore.
NEW QUESTION # 54
You are running a Kafka Streams application in a Docker container managed by Kubernetes, and upon application restart, it takes a long time for the docker container to replicate the state and get back to processing the dat a. How can you improve dramatically the application restart?
- A. Mount a persistent volume for your RocksDB
- B. Increase the number of Streams threads
- C. Increase the number of partitions in your inputs topic
- D. Reduce the Streams caching property
Answer: A
Explanation:
Although any Kafka Streams application is stateless as the state is stored in Kafka, it can take a while and lots of resources to recover the state from Kafka. In order to speed up recovery, it is advised to store the Kafka Streams state on a persistent volume, so that only the missing part of the state needs to be recovered.
NEW QUESTION # 55
What is returned by a producer.send() call in the Java API?
- A. A Boolean indicating if the call succeeded
- B. Future<ProducerRecord> object
- C. Unit
- D. Future<RecordMetadata> object
Answer: D
Explanation:
Seehttps://kafka.apache.org/21/javadoc/org/apache/kafka/clients/producer/KafkaProducer.html
NEW QUESTION # 56
Which of these joins does not require input topics to be sharing the same number of partitions?
- A. KStream-GlobalKTable
- B. KTable-KTable join
- C. KStream-KTable join
- D. KStream-KStream join
Answer: A
Explanation:
GlobalKTables have their datasets replicated on each Kafka Streams instance and therefore no repartitioning is required
NEW QUESTION # 57
In stream processing applications, handling out-of-order data can be a challenge which could affect the implemented business logic.
Which type of Join handles out-of-order records?
- A. KTable-KTable
- B. KStream- KStream
- C. KTable-KStream
- D. KStream-GlobalKTable
Answer: B
NEW QUESTION # 58
You are managing the schema of data in a Kafka Topic using Schema Registry. You need to add new fields to the message schema. You need to select a compatibility type that allows you to add required fields, delete optional fields, and allows consumers to read all previous versions of the schema.
Which compatibility type is correct?
- A. BACKWARD
- B. FORWARD
- C. FORWARD_TRANSITIVE
- D. FULL_TRANSITIVE
Answer: D
NEW QUESTION # 59
You are building a consumer application that processes events from a Kafka topic. What is the most important metric to monitor to ensure real-time processing?
- A. BytesInPerSec
- B. MessagesInPerSec
- C. UnderReplicatedPartitions
- D. records-lag-max
Answer: D
Explanation:
This metric shows the current lag (number of messages behind the broker)
NEW QUESTION # 60
An application is writing AVRO messages using Schema Registry to topic t1. During this process, the Schema Registry becomes unavailable for a few seconds.
What is the expected impact to the application?
- A. Since messages are cached by the producer, the application will only get an error if the producer is sending the batch at that time.
- B. The application may not have any impact, unless it is writing messages with a new Schema Definition.
- C. Since the broker will eventually replicate the message Schema, there will not be an error.
- D. All messages within that time will receive an error.
Answer: B
NEW QUESTION # 61
You need to set alerts on key broker metrics to trigger notifications when the cluster is unhealthy.
Which are three minimum broker metrics to monitor?
(Select three.)
- A. kafka.controller:type=KafkaController,name=OfflinePartitionsCount
- B. kafka.controller:type=KafkaController,name=ActiveControllerCount
- C. kafka.controller:type=ControllerStats,name=UncleanLeaderElectionsPerSec
- D. kafka.controller:type=KafkaController,name=TopicsToDeleteCount
- E. kafka.controller:type=KafkaController,name=LastCommittedRecordOffset
Answer: A,B,C
Explanation:
These three metrics are critical for cluster health:
OfflinePartitionsCount: Indicates partitions without active leaders - a sign of broker failure.
ActiveControllerCount: There should be exactly one active controller. A count # 1 signals controller failure.
UncleanLeaderElectionsPerSec: Tracks leader elections where out-of-sync replicas were selected - risky for data loss.
From Kafka Monitoring Documentation:
"Offline partitions and unclean leader elections should trigger alerts. Also, ensure a single active controller is running." A is about topics pending deletion - not critical.
E is a per-topic record metric, not broker-level.
Reference: Kafka Monitoring > Key JMX Metrics
NEW QUESTION # 62
Which of the following is NOT a supported serialization format in ksqlDB?
- A. Avro
- B. BSON
- C. Delimited
- D. Protobuf
Answer: B
NEW QUESTION # 63
You need to consume messages from Kafka using the command-line interface (CLI).
Which command should you use?
- A. kafka-console-consumer
- B. kafka-consume
- C. kafka-consumer
- D. kafka-get-messages
Answer: A
Explanation:
The official CLI utility for consuming messages from Kafka topics is kafka-console-consumer.sh. It connects to the broker, consumes messages, and prints them to standard output.
FromKafka CLI Tools Documentation:
"kafka-console-consumer.sh is used to read data from a Kafka topic and write it to standard output." The other options are not valid Kafka CLI tools.
Reference:Apache Kafka Documentation > kafka-console-consumer.sh
NEW QUESTION # 64
You need to collect logs from a host and write them to a Kafka topic named 'logs-topic'. You decide to use Kafka Connect File Source connector for this task.
What is the preferred deployment mode for this connector?
- A. Distributed mode
- B. SingleCluster mode
- C. Standalone mode
- D. Parallel mode
Answer: C
Explanation:
Kafka Connect can run instandalone modeordistributed mode. For simple tasks likereading logs from a file on a single host,standalone modeis recommended.
FromKafka Connect User Guide:
"Standalone mode is useful when running connectors on a single machine (e.g., for development or simple deployments like log collection from a local file)." Distributed mode is preferred for scalability and fault tolerance but overkill for this use case.
Reference:Kafka Connect User Guide > Deployment Modes
NEW QUESTION # 65
If I produce to a topic that does not exist, and the broker setting auto.create.topic.enable=true, what will happen?
- A. Kafka will automatically create the topic with num.partitions=#of brokers and replication.factor=3
- B. Kafka will automatically create the topic with the broker settings num.partitions and default.replication.factor
- C. Kafka will automatically create the topic with 1 partition and 1 replication factor
- D. Kafka will automatically create the topic with the indicated producer settings num.partitions and default.replication.factor
Answer: B
Explanation:
The broker settings comes into play when a topic is auto created
NEW QUESTION # 66
Two consumers share the same group.id (consumer group id). Each consumer will
- A. Read mutually exclusive offsets blocks on all the partitions
- B. Read all data from all partitions
- C. Read all the data on mutual exclusive partitions
Answer: C
Explanation:
Each consumer is assigned a different partition of the topic to consume.
NEW QUESTION # 67
You have a Kafka consumer in production actively reading from a critical topic.
You need to update the offset of your consumer to start reading from the beginning of the topic.
Which action should you take?
- A. Temporarily configure the topic's retention.ms parameter to 0 to empty the topic.
- B. Update the consumer group's offset to the earliest position using the kafka-consumer-groups CLI tool.
- C. Update the consumer configuration by setting auto.offset.reset=earliest.
- D. Start a new consumer application with the same consumer group id.
Answer: B
Explanation:
To reset offsets for an existing consumer group, you must use the kafka-consumer-groups.sh tool with the -- reset-offsets and --to-earliest flags.
From Kafka Consumer Group Tool Documentation:
"You can use the kafka-consumer-groups tool to reset offsets for a consumer group. This is required if the consumer has already committed offsets." Setting auto.offset.reset=earliest only works if no committed offset exists.
Starting a new consumer with the same group won't reset offsets.
Retention settings don't affect committed offsets.
Reference: Kafka Consumer Group CLI Tool
NEW QUESTION # 68
Where are the ACLs stored in a Kafka cluster by default?
- A. Under Zookeeper node /kafka-acl/
- B. Inside the Zookeeper's data directory
- C. In Kafka topic __kafka_acls
- D. Inside the broker's data directory
Answer: D
Explanation:
ACLs are stored in Zookeeper node /kafka-acls/ by default.
NEW QUESTION # 69
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 # 70
Select all the way for one consumer to subscribe simultaneously to the following topics - topic.history, topic.sports, topic.politics? (select two)
- A. consumer.subscribe(Arrays.asList("topic.history", "topic.sports", "topic.politics"));
- B. consumer.subscribe(Pattern.compile("topic\..*"));
- C. consumer.subscribePrefix("topic.");
- D. consumer.subscribe("topic.history"); consumer.subscribe("topic.sports"); consumer.subscribe("topic.politics");
Answer: A,B
Explanation:
Multiple topics can be passed as a list or regex pattern.
NEW QUESTION # 71
What is the default maximum size of a message the Apache Kafka broker can accept?
- A. 1MB
- B. 2MB
- C. 5MB
- D. 10MB
Answer: A
Explanation:
The default maximum message size that a Kafka broker accepts is1MB (1,048,576 bytes), controlled by the config propertymessage.max.bytes.
FromKafka Broker Configuration Docs:
"The default maximum message size is 1MB. To accept larger messages, configure message.max.bytes and the producer's max.request.size." Producers also have a matching limit via max.request.size, and consumers via fetch.message.max.bytes.
Reference:Kafka Broker Configuration > message.max.bytes
NEW QUESTION # 72
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