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| Certification Vendor: | Confluent |
|---|---|
| Exam Name: | Confluent Certified Developer for Apache Kafka Certification Examination |
| Exam Number: | CCDAK |
| Certificate Validity Period: | 2 years |
| Exam Format: | Matching, List order, Multiple-choice |
| Available Languages: | English |
| Real Exam Qty: | 60 |
| Exam Duration: | 90 minutes |
| Passing Score: | Pass / Fail |
| Related Certifications: | Confluent Cloud Certified Operator (CCAC) Confluent Certified Administrator for Apache Kafka (CCAAK) |
| Exam Price: | $150 USD |
| Recommended Training: | Confluent Documentation Confluent Developer Training for Apache Kafka |
| Exam Registration: | Confluent Certification Portal |
| Sample Questions: | Confluent CCDAK Sample Questions |
| Exam Way: | Remote proctored online exam; requires webcam, microphone, Google Chrome browser |
| Pre Condition: | No mandatory prerequisites; recommended experience: 6+ months developing applications with Apache Kafka |
| Official Syllabus URL: | https://www.confluent.io/certification/ |
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Confluent CCDAK Certification Exam is designed to be challenging and covers a variety of topics, including Kafka core concepts, Kafka ecosystem, developing Kafka producers and consumers, Kafka Connect, Kafka Streams, and several other application development and testing topics. Candidates who pass the certification exam will have demonstrated proficiency in working with the Confluent platform to develop and deploy Kafka-based applications, creating and using Avro schemas, and leveraging various Kafka tools and frameworks.
NEW QUESTION # 68
In Kafka, every broker... (select three)
Answer: A,E,F
Explanation:
Kafka topics are divided into partitions and spread across brokers. Each brokers knows about all the metadata and each broker is a bootstrap broker, but only one of them is elected controller
NEW QUESTION # 69
(You are experiencing low throughput from a Java producer.
Kafka producer metrics show a low I/O thread ratio and low I/O thread wait ratio.
What is the most likely cause of the slow producer performance?)
Answer: C
Explanation:
According to the official Apache Kafka producer metrics documentation, the I/O thread ratio and I/O thread wait ratio indicate how busy the producer's network I/O thread is and how much time it spends waiting. When both metrics are low, it means the I/O thread is underutilized and not blocked on network operations.
This strongly suggests that the bottleneck is not network-related. If there were network latency or a bad data link (Option B), the I/O thread wait ratio would be high. Similarly, large batches (Option A) or compression (Option D) generally increase throughput and would result in higher I/O utilization.
An expensive or blocking callback function (for example, heavy logic in onCompletion()) executes in the producer's sender thread context. Kafka documentation explicitly warns that slow callbacks can throttle producer throughput because they delay the processing of acknowledgements and subsequent sends.
Therefore, the most likely and documented cause of low producer throughput with low I/O utilization is expensive callback logic in the producer code.
NEW QUESTION # 70
How much should be the heap size of a broker in a production setup on a machine with 256 GB of RAM, in PLAINTEXT mode?
Answer: C
Explanation:
In Kafka, a small heap size is needed, while the rest of the RAM goes automatically to the page cache (managed by the OS). The heap size goes slightly up if you need to enable SSL
NEW QUESTION # 71
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?
Answer: A
Explanation:
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!
NEW QUESTION # 72
You are receiving orders from different customer in an "orders" topic with multiple partitions. Each message has the customer name as the key. There is a special customer named ABC that generates a lot of orders and you would like to reserve a partition exclusively for ABC. The rest of the message should be distributed among other partitions. How can this be achieved?
Answer: C
Explanation:
A Custom Partitioner allows you to easily customise how the partition number gets computed from a source message.
NEW QUESTION # 73
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