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| Certification Vendor: | Confluent |
|---|---|
| Exam Name: | Confluent Certified Developer for Apache Kafka (CCDAK) Certification Exam |
| Exam Number: | CCDAK |
| Related Certifications: | Confluent Certified Administrator for Apache Kafka (CCAK) Confluent Certified Developer for Apache Kafka (CCDAK) |
| Certificate Validity Period: | 3 years |
| Available Languages: | English |
| Real Exam Qty: | 60 |
| Passing Score: | 70% |
| Exam Format: | Multiple select, Multiple choice |
| Exam Duration: | 90 minutes |
| Exam Price: | $150 USD |
| Recommended Training: | Kafka Fundamentals Course Apache Kafka Developer Training (Confluent) |
| Exam Registration: | Confluent Training and Exams Confluent Certification Portal |
| 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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To become certified as a Confluent CCDAK certified developer for Apache Kafka, candidates must pass a rigorous online examination. CCDAK exam is computer-based and consists of 90 multiple choice questions, and has a time limit of 120 minutes. CCDAK exam is designed to challenge the technical skills and knowledge needed for creating and managing Apache Kafka applications using Confluent's platform. Passing the Confluent CCDAK Exam demonstrates a proficiency in working with Kafka streams in a distributed environment using Confluent platform, allowing candidates to leverage this certification to advance their careers.
NEW QUESTION # 25
Which two producer exceptions are examples of the class RetriableException? (Select two.)
Answer: A,B
Explanation:
BothLeaderNotAvailableExceptionandNotEnoughReplicasExceptionare subclasses of RetriableException, which representstransient issuesthat may succeed upon retry.
FromApache Kafka Java Client Documentation:
"RetriableException indicates that the request can be retried. This includes network errors and certain broker- side failures like leader not available or not enough replicas."
* RecordTooLargeException and AuthorizationException arenon-retriablebecause they represent client misconfigurations or access issues.
Reference:Kafka Error Handling Guide > Retriable vs. Non-Retriable Exceptions
NEW QUESTION # 26
Where are the dynamic configurations for a topic stored?
Answer: C
Explanation:
Dynamic topic configurations are maintained in Zookeeper.
NEW QUESTION # 27
How will you read all the messages from a topic in your KSQL query?
Answer: C
Explanation:
Consumers can set auto.offset.reset property to earliest to start consuming from beginning. For KSQL, SET 'auto.offset.reset'='earliest';
NEW QUESTION # 28
When auto.create.topics.enable is set to true in Kafka configuration, what are the circumstances under which a Kafka broker automatically creates a topic? (select three)
Answer: B,C,D
Explanation:
A kafka broker automatically creates a topic under the following circumstances- When a producer starts writing messages to the topic - When a consumer starts reading messages from the topic - When any client requests metadata for the topic
NEW QUESTION # 29
(A stream processing application tracks user activity in online shopping carts, including items added, removed, and ordered throughout the day for each user.
You need to capture data to identify possible periods of user inactivity.
Which type of Kafka Streams window should you use?)
Answer: D
Explanation:
According to the official Apache Kafka Streams documentation, session windows are specifically designed to model periods of activity separated by gaps of inactivity. A session window groups events that occur close together in time and closes when no new events arrive within a defined inactivity gap.
In this use case, tracking user shopping cart behavior requires detecting when a user becomes inactive (for example, stops adding or removing items). Session windows naturally represent this pattern by creating a new window when activity resumes after a period of silence. This makes them ideal for modeling user sessions, cart abandonment detection, and inactivity analysis.
Hopping and tumbling windows (Options B and C) are fixed-time windows and are not well-suited for detecting inactivity, as they segment data based on clock time rather than behavior. Sliding windows are primarily used for continuous joins and do not explicitly model inactivity gaps.
Therefore, session windows are the correct and officially recommended choice for identifying periods of user inactivity in Kafka Streams applications.
NEW QUESTION # 30
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