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Confluent CCDAK Exam Overview:

Certification Vendor:Confluent
Exam Name:Confluent Certified Developer for Apache Kafka Certification Examination
Exam Number:CCDAK
Passing Score:Pass / Fail
Exam Price:$150 USD
Available Languages:English
Exam Format:Multiple-choice, List order, Matching
Exam Duration:90 minutes
Related Certifications:Confluent Cloud Certified Operator (CCAC)
Confluent Certified Administrator for Apache Kafka (CCAAK)
Certificate Validity Period:2 years
Real Exam Qty:60
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/

>> CCDAK測試 <<

CCDAK測試和資格考試中的領先材料供應商&CCDAK權威認證

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Confluent CCDAK 認證展示了開發者在 Apache Kafka 上的專業知識,並能提升他們的職業前景。該認證在全球范圍內得到認可,可以幫助開發者脫穎而出。獲得認證的開發者還可以加入 Confluent 社區,訪問獨家資源,如網絡研討會、論壇和聚會。他們還可以在其專業檔案和簡歷中展示其認證。

此認證是開發人員展示他們對 Kafka 及其生態系統組件的理解,以及他們在構建基於 Kafka 的應用程序方面的實踐經驗的機會。認證考試驗證了候選人的 Kafka 知識、技能和開發解決實際 Kafka 使用情況的能力。此外,CCDAK 認證得到全球公認,使其成為有助於軟件開發人員獲得下一個 Kafka 開發角色的有價值的認證。

CCDAK認證考試旨在測試使用Apache Kafka構建可擴展的實時數據處理應用程序的開發人員的技能和知識。考試涵蓋了多個主題,包括Kafka基礎知識,Kafka架構,Kafka API和Kafka流。該考試旨在幫助開發人員展示他們在使用Kafka方面的熟練程度,並在求職市場中脫穎而出。

最新的 Confluent Certified Developer CCDAK 免費考試真題 (Q33-Q38):

問題 #33
You have a Kafka client application that has real-time processing requirements.
What are the most important metrics available in Kafka that you should monitor?

答案:B


問題 #34
(A consumer application runs once every two weeks and reads from a Kafka topic.
The last time the application ran, the last offset processed was 217.
The application is configured with auto.offset.reset=latest.
The current offsets in the topic start at 318 and end at 588.
Which offset will the application start reading from when it starts up for its next run?)

答案:C

解題說明:
According to the official Apache Kafka consumer documentation, the auto.offset.reset configuration is only applied when a consumer has no valid committed offset for a partition. In this scenario, the consumer last committed offset 217, but the topic's earliest available offset is now 318. This means the previously committed offset is out of range, most likely due to topic retention cleanup.
When a committed offset is out of range, Kafka treats it as invalid and applies the auto.offset.reset policy.
Since the consumer is configured with auto.offset.reset=latest, it will start reading from the end of the log, not from the earliest available offset.
Kafka defines the "latest" offset as the log end offset, which is the offset of the next message to be written.
Given that the topic's offsets end at 588, the next offset is 589.
Therefore, the consumer will start reading from offset 589, skipping all existing records in the topic, which aligns with the documented Kafka consumer behavior.


問題 #35
You are using JDBC source connector to copy data from 2 tables to two Kafka topics. There is one connector created with max.tasks equal to 2 deployed on a cluster of 3 workers. How many tasks are launched?

答案:A

解題說明:
we have two tables, so the max number of tasks is 2


問題 #36
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?

答案:B

解題說明:
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.


問題 #37
(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?)

答案:A

解題說明:
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.


問題 #38
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此外,這些KaoGuTi CCDAK考試題庫的部分內容現在是免費的:https://drive.google.com/open?id=1IE7ZcWQm01uzk6BlJNN1bZZZ8YW_VtHi