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Confluent CCDAK certification exam is an excellent opportunity for developers to prove their skills and knowledge in Kafka development using Confluent platform. Confluent Certified Developer for Apache Kafka Certification Examination certification exam is a globally recognized certification that validates the candidate's expertise in Kafka development and Confluent platform. Confluent Certified Developer for Apache Kafka Certification Examination certification exam is a must-have for developers who want to enhance their career prospects and demonstrate their proficiency in Kafka development using Confluent platform.
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Before taking the Confluent CCDAK Exam, developers should have a good understanding of Kafka fundamentals, including topics, partitions, brokers, producers, consumers, and Kafka Connect. They should also be proficient in Java or Scala programming languages and have experience working with REST APIs and command-line interfaces. It is recommended that developers attend the Confluent Developer Training or have equivalent experience before taking the exam. Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions (Q245-Q250):NEW QUESTION # 245
You are writing a producer application and need to ensure proper delivery. You configure the producer with acks=all.
Which two actions should you take to ensure proper error handling?
(Select two.)
A. Check that producer.send() returned a RecordMetadata object and is not null.
B. Surround the call of producer.send() with a try/catch block to catch KafkaException.
C. Check the value of ProducerRecord.status().
D. Use a callback argument in producer.send() where you check delivery status.
Answer: B,D
Explanation:
For proper delivery handling with acks=all:
* Usecallbackto log or act on success/failure.
* Usetry/catchto handle synchronous exceptions like serialization errors or network failures.
FromKafka Producer Documentation:
"Errors can be caught either via the returned Future<RecordMetadata> or via the callback interface. For fatal errors, use a try/catch block around the send call." Option B is incorrect because send() returns a Future, not RecordMetadata directly.
Option D is invalid - ProducerRecord has no method called status().
Reference:Kafka Producer Error Handling and Callback APIs
NEW QUESTION # 246
Which KSQL queries write to Kafka?
A. CREATE STREAM WITH <topic> and CREATE TABLE WITH <topic>
B. SHOW STREAMS and EXPLAIN <query> statements
C. COUNT and JOIN
D. CREATE STREAM AS SELECT and CREATE TABLE AS SELECT
Answer: A,D
Explanation:
SHOW STREAMS and EXPLAIN <query> statements run against the KSQL server that the KSQL client is connected to. They don't communicate directly with Kafka. CREATE STREAM WITH <topic> and CREATE TABLE WITH <topic> write metadata to the KSQL command topic. Persistent queries based on CREATE STREAM AS SELECT and CREATE TABLE AS SELECT read and write to Kafka topics.
Non-persistent queries based on SELECT that are stateless only read from Kafka topics, for example:
SELECT A FROM foo WHERE A;
Non-persistent queries that are stateful read and write to Kafka, for example, COUNT and JOIN. The data in Kafka is deleted automatically when you terminate the query with CTRL-C.
NEW QUESTION # 247
You have a topic with four partitions. The application reads from it using two consumers in a single consumer group.
Processing is CPU-bound, and lag is increasing.
What should you do?
A. Increase the max.poll.records property of consumers.
B. Decrease the max.poll.records property of consumers.
C. Add more partitions to the topic to increase the level of parallelism of the processing.
D. Add more consumers to increase the level of parallelism of the processing.
Answer: D
Explanation:
If the application isCPU-boundandlagging, addingmore consumersto the group will allow betterparallel processing, especially since the topic has4 partitions, allowing up to 4 active consumers.
FromKafka Consumer Group Docs:
"Kafka achieves parallelism by distributing partitions across consumers in a group. Adding consumers helps reduce lag if partitions are underutilized."
* B may help but requires repartitioning and coordination.
* C or D affects how much data is polled, not how fast it's processed.
Reference:Kafka Consumer Concepts > Parallelism and Scaling
NEW QUESTION # 248
Which actions will trigger partition rebalance for a consumer group? (select three)
A. Add a new consumer to consumer group
B. Remove a broker from the cluster
C. Increase partitions of a topic
D. A consumer in a consumer group shuts down
Answer: A,C,D
Explanation:
Add a broker to the cluster
Explanation:
Rebalance occurs when a new consumer is added, removed or consumer dies or paritions increased.
NEW QUESTION # 249
What is the risk of increasing max.in.flight.requests.per.connection while also enabling retries in a producer?
A. Reduce throughput
B. Message order not preserved
C. Less resilient
D. At least once delivery is not guaranteed
Answer: B
Explanation:
Some messages may require multiple retries. If there are more than 1 requests in flight, it may result in messages received out of order. Note an exception to this rule is if you enable the producer settingenable.
idempotence=true which takes care of the out of ordering case on its own. Seehttps://issues.apache.org/jira
/browse/KAFKA-5494