[Oct 17, 2025] CCDAK Questions Truly Valid For Your Confluent Exam!
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Preparing for the CCDAK certification exam requires a combination of theoretical knowledge and practical experience. Candidates should have a solid understanding of Kafka concepts and architecture, as well as hands-on experience working with Kafka clusters and building Kafka applications. There are many resources available to help candidates prepare for the exam, including online courses, practice exams, and study materials.
NEW QUESTION # 65
What is the risk of increasing max.in.flight.requests.per.connection while also enabling retries in a producer?
- A. Message order not preserved
- B. Reduce throughput
- C. Less resilient
- D. At least once delivery is not guaranteed
Answer: A
Explanation:
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
NEW QUESTION # 66
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. key.serializer
- B. bootstrap.servers
- C. value.serializer
- D. partition
- E. key
- F. value
Answer: A,B,C
Explanation:
Both key and value serializer are mandatory.
NEW QUESTION # 67
A topic receives all the orders for the products that are available on a commerce site. Two applications want to process all the messages independently - order fulfilment and monitoring. The topic has 4 partitions, how would you organise the consumers for optimal performance and resource usage?
- A. Create four consumers in the same group, one for each partition - two for fulfilment and two for monitoring
- B. Create two consumers groups for two applications with 8 consumers in each
- C. Create 8 consumers in the same group with 4 consumers for each application
- D. Create two consumer groups for two applications with 4 consumers in each
Answer: D
Explanation:
two partitions groups - one for each application so that all messages are delivered to both the application. 4 consumers in each as there are 4 partitions of the topic, and you cannot have more consumers per groups than the number of partitions (otherwise they will be inactive and wasting resources)
NEW QUESTION # 68
To prevent network-induced duplicates when producing to Kafka, I should use
- A. batch.size=1
- B. enable.idempotence=true
- C. retries=200000
- D. max.in.flight.requests.per.connection=1
Answer: B
Explanation:
Producer idempotence helps prevent the network introduced duplicates. More details herehttps://cwiki.apache.
org/confluence/display/KAFKA/Idempotent+Producer
NEW QUESTION # 69
Which of the following is a push query?
- A. SELECT *
FROM NUMBER_OF_TESTS
WHERE ID='10'; - B. SELECT windowstart, windowend, item_id, SUM(quantity)
FROM orders
WINDOW TUMBLING (SIZE 20 SECONDS)
GROUP BY item_id
EMIT CHANGES; - C. CREATE TABLE LEFT_TABLE (ID BIGINT PRIMARY KEY, NAME varchar, VALUE bigint) WITH (kafka_topic='left_topic', value_format='JSON', partitions=4);
- D. CREATE STREAM STUDENTS (ID STRING KEY, SCORE INT)
WITH (kafka_topic='students_topic', value_format='JSON', partitions=4);
Answer: B
NEW QUESTION # 70
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 # 71
Which SMTs is used to change the data type?
- A. org.apache.kafka.connect.transforms.Cast
- B. io.confluent.connect.transforms.TombstoneHandler
- C. org.apache.kafka.connect.transforms.TimestampRouter
- D. org.apache.kafka.connect.transforms.Flatten
Answer: A
NEW QUESTION # 72
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 # 73
In Avro, removing or adding a field that has a default is a __ schema evolution
- A. breaking
- B. backward
- C. full
- D. forward
Answer: C
Explanation:
Clients with new schema will be able to read records saved with old schema and clients with old schema will be able to read records saved with new schema.
NEW QUESTION # 74
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. The producer is sending large batches of messages.
- C. The producer code has an expensive callback function.
- D. There is a bad data link layer (layer 2) connection from the producer to the cluster.
Answer: C
Explanation:
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
NEW QUESTION # 75
Which statement is true about how exactly-once semantics (EOS) work in Kafka Streams?
- A. EOS in Kafka Streams is implemented by creating a separate Kafka topic for deduplication of all messages processed by the application.
- B. Kafka Streams provides EOS by periodically checkpointing state stores and replaying changelogs to recover only unprocessed messages during failure.
- C. Kafka Streams disables log compaction on internal changelog topics to preserve all state changes for potential recovery.
- D. EOS in Kafka Streams relies on transactional producers to atomically commit state updates to changelog topics and output records to Kafka.
Answer: D
Explanation:
Kafka Streams usestransactional producersto guaranteeexactly-once semantics (EOS). This ensures that both theoutput recordsandstate store updatesare committed atomically, avoiding duplication or partial writes.
FromKafka Streams Documentation > Processing Guarantees:
"Kafka Streams leveragesKafka's transactional APIsto 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
NEW QUESTION # 76
Which two statements about Kafka Connect Single Message Transforms (SMTs) are correct?
(Select two.)
- A. SMT functionality is included within Kafka Connect converters.
- B. SMTs are often used to join multiple records from a source data system into a single Kafka record.
- C. Multiple SMTs can be chained together and act on source or sink messages.
- D. Masking data is a good example of an SMT.
Answer: C,D
Explanation:
SMTs (Single Message Transforms) arelightweight transformationsapplied to individual messages as they pass through Kafka Connect.
* Chaining SMTs: You can applymultiple SMTsin sequence by defining them in order in the connector config.
* Masking or modifying fieldsis acommon use case(e.g., redacting sensitive data).
FromKafka Connect Documentation:
"Single Message Transforms (SMTs) are applied to individual messages. You can chain multiple SMTs together."
* SMTsdo not perform joins(B is incorrect).
* Converters and SMTs areseparate concerns(D is incorrect).
Reference:Kafka Connect Transformations Guide
NEW QUESTION # 77
Which of the following errors are retriable from a producer perspective? (select two)
- A. MESSAGE_TOO_LARGE
- B. INVALID_REQUIRED_ACKS
- C. NOT_LEADER_FOR_PARTITION
- D. NOT_ENOUGH_REPLICAS
- E. TOPIC_AUTHORIZATION_FAILED
Answer: C,D
Explanation:
Both of these are retriable errors, others non-retriable errors. See the full list of errors and their "retriable" status herehttps://kafka.apache.org/protocol#protocol_error_codes
NEW QUESTION # 78
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. The first message will always be written to partition 0.
- C. All messages will be written to the same log segment.
- D. Messages will be distributed among all the topic partitions with strict ordering.
Answer: A
Explanation:
If amessage key is not provided, Kafka's default partitioner usesround-robin distributionacross available partitions.
FromKafka Producer Design:
"If no key is provided, the default partitioner distributes messages round-robin to available partitions."
* A is incorrect - not all go to partition 0.
* C is invalid - Kafka doesn't group messages into one segment without a key.
* D is false - ordering isnot preservedacross partitions without a key.
Reference:Kafka Producer Partitioner Behavior
NEW QUESTION # 79
Which partition assignment minimizes partition movements between two assignments?
- A. RangeAssignor
- B. StickyAssignor
- C. PartitionAssignor
- D. RoundRobinAssignor
Answer: B
Explanation:
TheStickyAssignortries to minimize partition movement bypreserving existing assignmentsas much as possible while still achieving a balanced assignment. This improvesconsumer stabilityand reduces rebalances.
From theKafka Consumer Assignor Documentation:
"The StickyAssignor attempts topreserve as many existing assignments as possible, which helps minimize partition movement between rebalances."
* RoundRobinAssignor focuses on even distribution, not stability.
* RangeAssignor groups partitions by topic and assigns them consecutively, but can lead to imbalances.
* PartitionAssignor is an abstract base class, not an assignor used directly.
Reference:Kafka Consumer Assignor Docs
NEW QUESTION # 80
When auto.create.topics.enable is set to true in Kafka configuration, what are the circumstances under which a Kafka broker automatically creates a topic? (select three)
- A. Client alters number of partitions of a topic
- B. Consumer reads message from a topic
- C. Client requests metadata for a topic
- D. Producer sends message to a topic
Answer: B,C,D
Explanation:
A kafka broker automatically creates a topic under the following circumstances- When a producer starts writing messages to the topic - When a consumer starts reading messages from the topic - When any client requests metadata for the topic
NEW QUESTION # 81
There are 3 producers writing to a topic with 5 partitions. There are 10 consumers consuming from the topic as part of the same group. How many consumers will remain idle?
- A. 0
- B. None
- C. 1
- D. 2
Answer: C
Explanation:
One consumer per partition assignment will keep 5 consumers idle.
NEW QUESTION # 82
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 number of bytes that overflowed beyond a producer batch of messages
- B. The ID of the partition to which the message was committed
- C. Its position in the producer's batch of messages
- D. The sequential ID of the message committed into a partition
Answer: D
Explanation:
The offset in the RecordMetadata object returned by the producerrepresents the position of the record in the partition- i.e., thesequential IDassigned by Kafka once the message is committed.
FromKafka Producer API Documentation:
"The offset is the position of the record in the partition. This is a unique, sequential number assigned by the broker."
* D refers to metadata.partition(), not offset().
* B and C are unrelated to how Kafka handles committed offsets.
Reference:Kafka Producer Java API > RecordMetadata
NEW QUESTION # 83
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Confluent, the company behind Apache Kafka, offers the CCDAK certification to developers who have demonstrated their proficiency in developing and managing Kafka-based applications. The CCDAK certification is a validation of the developer's expertise in building scalable and resilient Kafka-based applications. Confluent Certified Developer for Apache Kafka Certification Examination certification exam covers topics such as Kafka architecture, message processing, partitioning, replication, security, and monitoring.
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