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| Certification Vendor: | Confluent |
|---|---|
| Exam Name: | Confluent Certified Developer for Apache Kafka (CCDAK) Certification Exam |
| Exam Number: | CCDAK |
| Real Exam Qty: | 60 |
| Exam Format: | Multiple select, Multiple choice |
| Related Certifications: | Confluent Certified Administrator for Apache Kafka (CCAK) Confluent Certified Developer for Apache Kafka (CCDAK) |
| Available Languages: | English |
| Exam Duration: | 90 minutes |
| Exam Price: | $150 USD |
| Passing Score: | 70% |
| Certificate Validity Period: | 3 years |
| Recommended Training: | Apache Kafka Developer Training (Confluent) Kafka Fundamentals Course |
| Exam Registration: | Confluent Certification Portal Confluent Training and Exams |
| Sample Questions: | Confluent CCDAK Sample Questions |
| Exam Way: | Online proctored exam or authorized testing center |
| Pre Condition: | Basic understanding of Java or another JVM language and familiarity with distributed systems concepts is recommended. |
| Official Syllabus URL: | https://www.confluent.io/certification/ |
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Confluent CCDAK is a certification exam designed for individuals who want to demonstrate their expertise in Apache Kafka and related technologies. Confluent Certified Developer for Apache Kafka Certification Examination certification is offered by Confluent, the company that created Kafka, and is recognized globally as a standard for Kafka developers. Confluent CCDAK Certification Exam is a comprehensive examination that tests the knowledge and skills of developers in Kafka.
NEW QUESTION # 70
A stream processing application is consuming from a topic that has five partitions. You run three instances of your application, each with num.streams.threads set to five.
You need to identify the amount of total stream tasks that will be created and the amount that will be actively consuming messages from the input topic.
Which option is correct?
Answer: A
NEW QUESTION # 71
A Kafka producer application wants to send log messages to a topic that does not include any key. What are the properties that are mandatory to configure for the producer configuration? (select three)
Answer: A,C,E
Explanation:
Both key and value serializer are mandatory.
NEW QUESTION # 72
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: B
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 # 73
What are two examples of performance metrics?
(Select two.)
Answer: A,E
Explanation:
* fetch-rate: Measures how frequently consumers fetch data - critical for monitoring throughput.
* incoming-byte-rate: Shows how much data is being written to the broker per second - a key producer-side metric.
FromKafka JMX Metrics Guide:
"fetch-rate, incoming-byte-rate, request-latency are key performance metrics for producers and consumers."
* B, C, E, and F areapplication-layer business metrics, not Kafka performance metrics.
Reference:Kafka Monitoring > JMX Metrics
NEW QUESTION # 74
(You want to enrich the content of a topic by joining it with key records from a second topic.
The two topics have a different number of partitions.
Which two solutions can you use?
Select two.)
Answer: A,C
Explanation:
The Apache Kafka Streams documentation defines a co-partitioning requirement for KStream-KTable and KStream-KStream joins. Both input topics must have the same number of partitions and the same key partitioning strategy.
One valid solution is to use a GlobalKTable (Option A). A GlobalKTable is fully replicated to every Kafka Streams instance, removing the co-partitioning requirement. This approach is recommended when the reference data is relatively small and changes infrequently.
Another valid solution is to repartition one topic so that both topics have the same number of partitions (Option B). Kafka Streams provides repartition topics specifically for this purpose, allowing proper KStream- KTable joins.
Option C does not resolve the partition mismatch, as increasing instances does not change partitioning. Option D is incorrect because Kafka Streams does not automatically repartition both topics for joins; repartitioning must be explicitly configured.
Therefore, the correct and officially supported solutions are using a GlobalKTable and explicitly repartitioning one topic.
NEW QUESTION # 75
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