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The CCDAK certification exam is a comprehensive and globally recognized certification program that validates an individual's knowledge and skills in Apache Kafka technology. It is designed for developers who work with Kafka and want to demonstrate their expertise in the field. Achieving the certification is a valuable asset for both individuals and organizations, as it ensures that developers have the necessary skills to develop and maintain Kafka-based applications.
Confluent CCDAK (Confluent Certified Developer for Apache Kafka) certification exam is designed for software developers who want to demonstrate their proficiency in building Kafka-based applications. Kafka is an open-source distributed messaging system that allows applications to exchange messages in real-time. It offers a highly scalable, fault-tolerant, and reliable platform for building large-scale distributed systems. The CCDAK Exam Tests the candidate's ability to work with Kafka and its ecosystem components e.g., producer, consumer, connectors, and streams API.
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The CCDAK Certification Exam is a hands-on exam that requires developers to demonstrate their skills in a real-world scenario. CCDAK exam consists of a set of tasks that must be completed within a set time frame. The tasks are designed to test a developer's ability to work with Kafka and Confluent's tools to build and manage Kafka-based applications. CCDAK Exam is graded based on the number of tasks completed and the quality of the solutions provided.
NEW QUESTION # 88
There are 3 brokers in the cluster. You want to create a topic with a single partition that is resilient to one broker failure and one broker maintenance. What is the replication factor will you specify while creating the topic?
Answer: A
Explanation:
1 is not possible as it doesn't provide resilience to failure, 2 is not enough as if we take a broker down for maintenance, we cannot tolerate a broker failure, and 6 is impossible as we only have 3 brokers (RF cannot be greater than the number of brokers). Here the correct answer is 3
NEW QUESTION # 89
What is true about partitions? (select two)
Answer: B,C
Explanation:
Only one of the replicas is elected as partition leader. And a broker can definitely hold many partitions from the same topic on its disk, try creating a topic with 12 partitions on one broker!
NEW QUESTION # 90
Refer to the producer code below. It features a 'Callback' class with a method called 'onCompletion()'. When will the 'onCompletion()' method be Invoked?
producer.send(record, new MyCallback(record));
Answer: D
NEW QUESTION # 91
This schema excerpt is an example of which schema format?
package com.mycorp.mynamespace;
message SampleRecord {
int32 Stock = 1;
double Price = 2;
string Product_Name = 3;
}
Answer: D
Explanation:
This syntax is a clear match to Protocol Buffers (Protobuf). It defines a schema with fields, types, and tags, which is a format supported by Kafka when using Protobuf-based schema registry serialization.
From Confluent Schema Registry Docs:
"Kafka supports Protobuf serialization, where schemas are written in .proto files and include fields with tags." int32, double, and string are standard Protobuf types.
Avro uses JSON-style schema.
JSON Schema uses JSON object structure, not .proto.
YAML is unrelated.
Reference: Confluent Schema Registry for Protobuf
NEW QUESTION # 92
(You create a topic with five partitions.
What can you assume about messages read from that topic by a single consumer group?)
Answer: D
Explanation:
The Apache Kafka documentation explains that Kafka's parallelism model is based on topic partitions and consumer groups. Within a single consumer group, each partition can be assigned to at most one consumer at a time. As a result, the maximum level of parallelism for a consumer group is equal to the number of partitions in the topic.
In this scenario, the topic has five partitions, which means that up to five consumers in the same consumer group can actively consume data in parallel. If more than five consumers are added, the extra consumers will remain idle because there are no additional partitions to assign.
Option B is incorrect because Kafka does not enforce equal message consumption across partitions; message distribution depends on producers and partitioning strategy. Option C is false because partitions may be hosted on different brokers, and consumers fetch data from multiple brokers. Option D is incorrect because a single partition cannot be consumed by multiple consumers within the same group simultaneously.
Therefore, the only valid assumption is that a maximum of five consumers can actively consume messages in the same consumer group.
NEW QUESTION # 93
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