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Confluent CCDAK (Confluent Certified Developer for Apache Kafka Certification Examination) is a certification exam designed for developers who are interested in Apache Kafka, an open-source distributed event streaming platform. CCDAK exam is offered by Confluent, a company that provides a platform for managing and scaling real-time data streams. Confluent Certified Developer for Apache Kafka Certification Examination certification aims to validate the developer's knowledge and skills in building and managing Kafka applications.
Confluent CCDAK (Confluent Certified Developer for Apache Kafka) Certification Examination is a comprehensive test designed to evaluate the proficiency of developers in building and maintaining Apache Kafka-based applications. CCDAK exam is targeted towards software developers, data architects, and data engineers who are looking for a way to validate their knowledge and skills in building real-time, scalable, and fault-tolerant distributed systems using Apache Kafka.
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The CCDAK certification exam is an online, proctored exam that consists of multiple-choice questions and hands-on exercises. CCDAK exam is designed to be challenging, but fair, and is intended to test the knowledge and skills of developers who have experience working with Kafka. CCDAK exam is available to anyone who meets the prerequisites, which include experience working with Kafka and a solid understanding of distributed systems and programming concepts. The CCDAK Certification Exam is an excellent way for developers to demonstrate their expertise in Kafka and differentiate themselves in a competitive job market.
NEW QUESTION # 42
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: A
Explanation:
In Kafka Streams, the number of stream tasks = number of input partitions × num.stream.threads × 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 × 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 # 43
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?
Answer: C
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 # 44
A stream processing application is tracking the user activity of online shopping carts. The application tracks items added to the cart, items removed from the cart, and items ordered from the cart throughout the day for each user.
You need to capture the data to identify possible periods of user inactivity.
Which type of Kafka Streams window should you use?
Answer: B
NEW QUESTION # 45
You have a consumer group with default configuration settings reading messages from your Kafka cluster.
You want to optimize its throughput so the consumer group processes more messages in the same amount of time.
Which changes increase the consumption throughput?
Answer: B
NEW QUESTION # 46
What isn't a feature of the Confluent schema registry?
Answer: B
Explanation:
Data is stored on brokers.
NEW QUESTION # 47
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