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The CCDAK certification exam covers a wide range of topics, including Kafka architecture, Kafka core concepts, Kafka deployment, Kafka API, Kafka Connect, Kafka Streams, and Confluent Cloud. CCDAK exam consists of 60 multiple-choice questions, which must be completed within 90 minutes. To pass the exam, candidates must score 70% or higher. The CCDAK certification is valid for two years, after which the candidate must recertify to maintain their certification status. Achieving the CCDAK Certification demonstrates an individual's ability to build scalable, fault-tolerant, and high-performance streaming applications using Apache Kafka, making them a valuable asset to any organization working with data streaming technologies.
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The CCDAK certification is offered by Confluent, the company behind Apache Kafka. Confluent Certified Developer for Apache Kafka Certification Examination certification provides developers with a way to demonstrate their expertise in Kafka development and gain recognition from potential employers. The CCDAK certification is also a requirement for becoming a Confluent Certified Developer or Confluent Certified Administrator.
The CCDAK Certification Exam is open to developers of all levels of experience, from beginners to advanced users. However, it is recommended that candidates have some experience working with Kafka before taking the exam. Candidates who pass the exam receive a certification that demonstrates their proficiency in working with Kafka, which can be a valuable asset in their careers.
NEW QUESTION # 36
Which statement describes the storage location for a sink connector's offsets?
Answer: B
Explanation:
Kafka Connectsink connectorsuse thestandard Kafka consumer mechanismto track offsets, which means offsets are stored in the__consumer_offsets internal topic.
FromKafka 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 tosource connectorsor worker configuration.
* offsets.storage.topic is relevant forsource connectors.
Reference:Kafka Connect Concepts > Sink Connectors and Offset Storage
NEW QUESTION # 37
ksqIDB Lambda functions must be used inside what type of designated functions?
Answer: D
NEW QUESTION # 38
A stream processing application is consuming from a topic with five partitions. You run three instances of the application. Each instance has num.stream.threads=5.
You need to identify the number of stream tasks that will be created and how many will actively consume messages from the input topic.
Answer: C
Explanation:
In Kafka Streams, the number of stream tasks = number of input partitions ร num.stream.threads ร number of instances, but only as many as the number of partitions can actively consume at once.
However, in this case, Kafka Streams assigns one task per partition, and because there are 5 partitions and 15 threads (3 instances ร 5 threads), 15 tasks are created, and all 15 can be active depending on processing topology.
From Kafka Streams Developer Guide:
"Kafka Streams creates one task per input partition. If you increase the number of stream threads, it runs multiple tasks in parallel within a single instance." So, 15 stream tasks will be created and 15 will be actively consuming.
Reference: Apache Kafka Streams Documentation > Concepts > Tasks and Threads
NEW QUESTION # 39
You create a producer that writes messages about bank account transactions from tens of thousands of different customers into a topic.
Your consumers must process these messages with low latency and minimize consumer lag Processing takes ~6x longer than producing Transactions for each bank account must be processed in orderWhich strategy should you use?
Answer: B
Explanation:
To maintain message ordering per bank account, the message key must be the account number. Kafka guarantees ordering per partition, and the partition is selected based on the hash of the key.
From Kafka Producer Docs:
"All records with the same key will be sent to the same partition, and Kafka preserves the order of records in a partition." Using account number ensures:
All messages for the same account go to the same partition
Order is maintained
Workload is evenly distributed if account keys are varied
Other options break ordering or introduce unnecessary randomness.
Reference: Kafka Producer Concepts > Partitions and Keys
NEW QUESTION # 40
An application is consuming messages from Kafka.
The application logs show that partitions are frequently being reassigned within the consumer group.
Which two factors may be contributing to this?
(Select two.)
Answer: A,C
Explanation:
Frequentrebalancesin a consumer group occur when:
* Consumers are too slowand miss heartbeats (A), or
* Instances crash or restart, triggering group membership changes (D)
FromKafka Consumer Group Coordination Docs:
"If a consumer fails to send a heartbeat in time (due to slowness or crash), it is removed from the group, causing a rebalance." Option B (partition mismatch) affectsload balancing, not rebalance frequency.
Option C (broker storage) doesn't trigger consumer rebalances.
Reference:Kafka Consumer Group Coordination and Heartbeats
NEW QUESTION # 41
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