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Confluent Certified Developer for Apache Kafka Certification Examination: CCDAK Exam
"Confluent Certified Developer for Apache Kafka Certification Examination", also known as CCDAK exam, is a Confluent Certification. With the complete collection of questions and answers, ExamcollectionPass has assembled to take you through 92 Q&As to your CCDAK Exam preparation. In the CCDAK exam resources, you will cover every field and category in Confluent Certified Developer Certification helping to ready you for your successful Confluent Certification.
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Confluent CCDAK Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Apache Kafka Application Development | 28% | - Producer API: configuration, serialization, partitioning, error handling - Transactional messaging and idempotence - Schema management: Confluent Schema Registry, compatibility rules, evolution - Consumer API: configuration, offset management, consumer groups, rebalancing |
| Topic 2: Kafka Connect | 15% | - Connect architecture: workers, connectors, tasks, converters - Source and sink connectors configuration - Data transformation and integration patterns |
| Topic 3: Apache Kafka Fundamentals | 23% | - Kafka architecture and data flow - Delivery semantics: at-most-once, at-least-once, exactly-once - Core concepts: brokers, topics, partitions, replicas, offsets - Configuration basics and cluster setup |
| Topic 4: Apache Kafka Streams | 12% | - Error handling and state management - Streams API concepts: topology, processors, state stores - Windowing and time semantics - Stream processing operations: map, filter, join, aggregation |
| Topic 5: Application Testing | 8% | - Mocking and test utilities - Integration testing and test clusters - Unit testing producers and consumers |
| Topic 6: Application Observability | 13% | - Logging and monitoring metrics - Error diagnosis and troubleshooting - Tracing and performance tuning |
Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions:
You use Kafka Connect with the JDBC source connector to extract data from a large database and push it into Kafka.
The database contains tens of tables, and the current connector is unable to process the data fast enough.
You add more Kafka Connect workers, but throughput doesn't improve.
What should you do next?
- A. Increase the number of Kafka partitions for the topics.
- B. Increase the value of the connector's property tasks.max.
- C. Add more Kafka brokers to the cluster.
- D. Modify the database schemas to enable horizontal sharding.
Correct Answer: B 🗳️
Explanation: Only visible for ExamcollectionPass members. You can sign-up / login (it's free).
(Which configuration is valid for deploying a JDBC Source Connector to read all rows from the orders table and write them to the dbl-orders topic?)
- A. {"name": "jdbc-source","connector.class": "io.confluent.connect.jdbc.DdbcSourceConnector","tasks.
max": "1","connection.url": "jdbc:mysql://mysql:3306/dbl?user=user&useAutoAuth=true","topic.
prefix": "dbl-","table.whitelist": "orders"} - B. {"name": "jdbc-source","connector.class": "io.confluent.connect.jdbc.JdbcSourceConnector","tasks.
max": "1","connection.url": "jdbc:mysql://mysql:3306/dbl?user=user&password=pas","topic.prefix":
"dbl-","table.whitelist": "orders"} - C. {"name": "dbl-orders","connector.class": "io.confluent.connect.jdbc.DdbcSourceConnector","tasks.
max": "1","connection.url": "jdbc:mysql://mysql:3306/dbl?user=user&password=pas","topic.prefix":
"dbl-","table.blacklist": "ord*"} - D. {"name": "orders-connect","connector.class": "io.confluent.connect.jdbc.DdbcSourceConnector","tasks.
max": "1","connection.url": "jdbc:mysql://mysql:3306/dbl","topic.whitelist": "orders","auto.create":
"true"}
Correct Answer: B 🗳️
Explanation: Only visible for ExamcollectionPass members. You can sign-up / login (it's free).
An application is consuming messages from Kafka.
The application logs show that partitions are frequently being reassigned within the consumer group.
Which two factors may be contributing to this?
(Select two.)
- A. An instance of the application is crashing and being restarted.
- B. The number of partitions does not match the number of application instances.
- C. There is a storage issue on the broker.
- D. There is a slow consumer processing application.
Correct Answer: A,D 🗳️
Explanation: Only visible for ExamcollectionPass members. You can sign-up / login (it's free).
(You need to set alerts on key broker metrics to trigger notifications when a Kafka cluster is unhealthy.
What are three minimum broker metrics to monitor for cluster health?
Select three.)
- A. kafka.controller:type=KafkaController,name=ActiveControllerCount
- B. kafka.controller:type=KafkaController,name=TopicsToDeleteCount
- C. kafka.controller:type=KafkaController,name=LastCommittedRecordOffset
- D. kafka.controller:type=KafkaController,name=OfflinePartitionsCount
- E. kafka.controller:type=ControllerStats,name=UncleanLeaderElectionsPerSec
Correct Answer: A,D,E 🗳️
Explanation: Only visible for ExamcollectionPass members. You can sign-up / login (it's free).
You create a topic named stream-logs with:
A replication factor of 3
Four partitions
Messages that are plain logs without a keyHow will messages be distributed across partitions?
- A. All messages will be written to the same log segment.
- B. Messages will be distributed round-robin among all the topic partitions.
- C. Messages will be distributed among all the topic partitions with strict ordering.
- D. The first message will always be written to partition 0.
Correct Answer: B 🗳️
Explanation: Only visible for ExamcollectionPass members. You can sign-up / login (it's free).
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