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Most Recent Confluent CCDAK Exam Dumps
Prepare for the Confluent Certified Developer for Apache Kafka exam with our extensive collection of questions and answers. These practice Q&A are updated according to the latest syllabus, providing you with the tools needed to review and test your knowledge.
QA4Exam focus on the latest syllabus and exam objectives, our practice Q&A are designed to help you identify key topics and solidify your understanding. By focusing on the core curriculum, These Questions & Answers helps you cover all the essential topics, ensuring you're well-prepared for every section of the exam. Each question comes with a detailed explanation, offering valuable insights and helping you to learn from your mistakes. Whether you're looking to assess your progress or dive deeper into complex topics, our updated Q&A will provide the support you need to confidently approach the Confluent CCDAK exam and achieve success.
The questions for CCDAK were last updated on Mar 30, 2025.
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Which of the following setting increases the chance of batching for a Kafka Producer?
linger.ms forces the producer to wait to send messages, hence increasing the chance of creating batches
A kafka topic has a replication factor of 3 and min.insync.replicas setting of 2. How many brokers can go down before a producer with acks=1 can't produce?
min.insync.replicas does not impact producers when acks=1 (only when acks=all)
What Java library is KSQL based on?
KSQL is based on Kafka Streams and allows you to express transformations in the SQL language that get automatically converted to a Kafka Streams program in the backend
A customer has many consumer applications that process messages from a Kafka topic. Each consumer application can only process 50 MB/s. Your customer wants to achieve a target throughput of 1 GB/s. What is the minimum number of partitions will you suggest to the customer for that particular topic?
each consumer can process only 50 MB/s, so we need at least 20 consumers consuming one partition so that 50 * 20 = 1000 MB target is achieved.
You have a Kafka cluster and all the topics have a replication factor of 3. One intern at your company stopped a broker, and accidentally deleted all the data of that broker on the disk. What will happen if the broker is restarted?
Kafka replication mechanism makes it resilient to the scenarios where the broker lose data on disk, but can recover from replicating from other brokers. This makes Kafka amazing!
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