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Confluent Certified Developer for Apache Kafka (CCDAK) Certification Examination is a highly regarded certification program that is designed to test an individual’s knowledge of Apache Kafka and its associated technologies. Confluent Certified Developer for Apache Kafka Certification Examination certification program is offered by Confluent, a leading company in the field of event streaming platforms, and is aimed at developers who want to demonstrate their expertise in building applications that utilize the Kafka ecosystem.
The CCDAK Certification Exam is offered by Confluent, which is the company behind the popular open-source Apache Kafka project. CCDAK exam is designed to test developers' knowledge of Kafka concepts, including Kafka architecture, producers and consumers, message delivery semantics, and Kafka Streams. CCDAK exam is also designed to test developers' ability to build and manage Kafka clusters and write efficient Kafka applications.
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The CCDAK exam covers a wide range of topics, including Kafka architecture and components, data modeling and serialization, producer and consumer APIs, Kafka Connect, Kafka Streams, and security. CCDAK exam consists of 60 multiple-choice questions and must be completed within two hours. Candidates who pass the exam will receive a Confluent Certified Developer for Apache Kafka certificate, which is valid for two years. The CCDAK Certification is a rigorous exam that requires a deep understanding of Kafka's architecture and APIs, and passing it is a significant achievement for any developer.
NEW QUESTION # 63
A consumer has auto.offset.reset=latest, and the topic partition currently has data for offsets going from 45 to
2311. The consumer group never committed offsets for the topic before. Where will the consumer read from?
Answer: C
Explanation:
Latest means that data retrievals will start from where the offsets currently end
NEW QUESTION # 64
(What are two stateless operations in the Kafka Streams API?
Select two.)
Answer: A,C
Explanation:
In the Kafka Streams API, operations are classified as stateless or stateful based on whether they require maintaining local state stores. According to the official Kafka Streams documentation, stateless operations process each record independently, without storing or accessing prior records.
Filter is a stateless operation because it evaluates each record individually and decides whether to pass it downstream. It does not require any state or historical context.
GroupBy is also considered stateless because it merely repartitions the stream by assigning a new key and forwarding records to downstream processors. While it triggers the creation of an internal repartition topic, the GroupBy operation itself does not maintain a state store.
In contrast, Reduce is a stateful operation because it aggregates records over time and requires maintaining intermediate results in a state store. Similarly, Join operations are stateful because Kafka Streams must buffer and store records from one or both input streams or tables to perform the join within a defined time window.
Thus, the correct stateless operations are Filter and GroupBy, as documented in the Kafka Streams developer guide.
NEW QUESTION # 65
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
Answer: C
Explanation:
Here, you shouldn't commit offsets 11 or 10 as it would indicate that the message #10 has been processed successfully.
NEW QUESTION # 66
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.
Answer: A,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 # 67
(You are designing a stream pipeline to monitor the real-time location of GPS trackers, where historical location data is not required.
Each event has:
* Key: trackerId
* Value: latitude, longitude
You need to ensure that the latest location for each tracker is always retained in the Kafka topic.
Which topic configuration parameter should you set?)
Answer: A
Explanation:
According to the official Apache Kafka documentation, log compaction is the mechanism used to retain only the latest record for each key in a topic. By setting cleanup.policy=compact, Kafka ensures that older records with the same key are eventually removed, leaving only the most recent value for each key.
This behavior is exactly suited for use cases such as tracking the current state of an entity, including GPS tracker locations, user profiles, or configuration data. In this scenario, the key (trackerId) uniquely identifies a tracker, and the value represents its most recent latitude and longitude.
Option B (retention.ms=infinite) retains all historical data, which contradicts the requirement. Option C (min.
cleanable.dirty.ratio) controls when compaction runs, not what data is retained. Option D (retention.ms=0) would immediately delete all data and is unsafe.
Therefore, setting cleanup.policy=compact is the correct and officially documented solution to always retain the latest location per tracker.
NEW QUESTION # 68
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