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Confluent CCDAK certification demonstrates a developer’s expertise in Apache Kafka and can enhance their career prospects. Confluent Certified Developer for Apache Kafka Certification Examination certification is recognized globally and can help developers stand out from the competition. Certified developers can also join the Confluent Community and access exclusive resources, such as webinars, forums, and meetups. They can also showcase their certification on their professional profile and resume.
The CCDAK certification exam is a valuable credential for developers who work with Kafka. It not only validates their knowledge and skills but also enhances their career opportunities. The CCDAK certification is recognized by the industry and can help developers stand out in the job market. By investing time and effort in preparing for the CCDAK certification exam, developers can take their Kafka expertise to the next level.
NEW QUESTION # 65
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?
- A. Total time to serve requests to replica followers
- B. Consumer lag between the brokers and the consumers
- C. Consumer heartbeat rate to the group coordinator
- D. Aggregate incoming byte rate
Answer: B
NEW QUESTION # 66
If I want to send binary data through the REST proxy to topic "test_binary", it needs to be base64 encoded. A consumer connecting directly into the Kafka topic
- A. avro data
- B. binary data
- C. json data
- D. "test_binary" will receive
- E. base64 encoded data, it will need to decode it
Answer: B
Explanation:
On the producer side, after receiving base64 data, the REST Proxy will convert it into bytes and then send that bytes payload to Kafka. Therefore consumers reading directly from Kafka will receive binary data.
NEW QUESTION # 67
A producer is sending messages with null key to a topic with 6 partitions using the DefaultPartitioner. Where will the messages be stored?
- A. Any of the topic partitions
- B. Partition 0
- C. Partition 5
- D. The partition for the null key
Answer: C
Explanation:
Message with no keys will be stored with round-robin strategy among partitions.
NEW QUESTION # 68
What is accomplished by producing data to a topic with a message key?
- A. Kafka brokers allow you to add more partitions to a given topic, without impacting the data flow for existing keys.
- B. It provides a mechanism for encrypting messages at the partition level to ensure secure data transmission.
- C. Messages with the same key are routed to a deterministically selected partition, enabling order guarantees within that partition.
- D. Consumers can filter messages in real time based on the message key without processing unrelated messages.
Answer: C
Explanation:
When amessage keyis specified in Kafka, the producer uses apartitioner(typically the default hash-based partitioner) todeterministically map all records with the same key to the same partition. Kafka guarantees order within a partition, so this enables per-key ordering.
FromKafka Producer Concepts:
"If a key is present, the producer will always route records with the same key to the same partition. Kafka preserves the order of records within a partition." This is essential for ordered processing and join semantics.
Reference:Kafka Producer Design > Partitions and Keys
NEW QUESTION # 69
A consumer starts and has auto.offset.reset=latest, and the topic partition currently has data for offsets going from 45 to 2311. The consumer group has committed the offset 643 for the topic before. Where will the consumer read from?
- A. offset 45
- B. it will crash
- C. offset 2311
- D. offset 643
Answer: D
Explanation:
The offsets are already committed for this consumer group and topic partition, so the property auto.offset.reset is ignored
NEW QUESTION # 70
What are two examples of performance metrics?
(Select two.)
- A. incoming-byte-rate
- B. fetch-rate
- C. total-login-attempts
- D. Time of last failed login
- E. Number of active users
- F. Number of active user sessions
Answer: A,B
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 # 71
Compaction is enabled for a topic in Kafka by setting log.cleanup.policy=compact. What is true about log compaction?
- A. After cleanup, only one message per key is retained with the latest value
- B. After cleanup, only one message per key is retained with the first value
- C. Each message stored in the topic is compressed
- D. Kafka automatically de-duplicates incoming messages based on key hashes
Answer: A
Explanation:
Compaction changes the offset of messages
Explanation:
Log compaction retains at least the last known value for each record key for a single topic partition. All compacted log offsets remain valid, even if record at offset has been compacted away as a consumer will get the next highest offset.
NEW QUESTION # 72
You want to perform table lookups against a KTable everytime a new record is received from the KStream. What is the output of KStream-KTable join?
- A. KTable
- B. You choose between KStream or KTable
- C. Kstream
- D. GlobalKTable
Answer: C
Explanation:
Here KStream is being processed to create another KStream.
NEW QUESTION # 73
A stream processing application is tracking the user activity of online shopping carts. The application tracks items added to the cart, items removed from the cart, and items ordered from the cart throughout the day for each user.
You need to capture the data to identify possible periods of user inactivity.
Which type of Kafka Streams window should you use?
- A. Tumbling
- B. Sliding
- C. Session
- D. Hopping
Answer: C
NEW QUESTION # 74
A consumer is configured with enable.auto.commit=false. What happens when close() is called on the consumer object?
- A. The uncommitted offsets are committed
- B. The group coordinator will discover that the consumer stopped sending heartbeats. It will cause rebalance after session.timeout.ms
- C. A rebalance in the consumer group will happen immediately
Answer: C
Explanation:
Calling close() on consumer immediately triggers a partition rebalance as the consumer will not be available anymore.
NEW QUESTION # 75
Which of the following is true regarding thread safety in the Java Kafka Clients?
- A. One Consumer needs to run in one thread
- B. One Consumer can be safely used in multiple threads
- C. One Producer can be safely used in multiple threads
- D. One Producer needs to be run in one thread
Answer: A,C
Explanation:
KafkaConsumer is not thread-safe, KafkaProducer is thread safe.
NEW QUESTION # 76
Select all the way for one consumer to subscribe simultaneously to the following topics - topic.history, topic.sports, topic.politics? (select two)
- A. consumer.subscribe(Pattern.compile("topic\..*"));
- B. consumer.subscribe("topic.history"); consumer.subscribe("topic.sports"); consumer.subscribe("topic.politics");
- C. consumer.subscribe(Arrays.asList("topic.history", "topic.sports", "topic.politics"));
- D. consumer.subscribePrefix("topic.");
Answer: A,C
Explanation:
Multiple topics can be passed as a list or regex pattern.
NEW QUESTION # 77
You need to consume messages from Kafka using the command-line interface (CLI).
Which command should you use?
- A. kafka-console-consumer
- B. kafka-consumer
- C. kafka-consume
- D. kafka-get-messages
Answer: A
Explanation:
The official CLI utility for consuming messages from Kafka topics is kafka-console-consumer.sh. It connects to the broker, consumes messages, and prints them to standard output.
FromKafka CLI Tools Documentation:
"kafka-console-consumer.sh is used to read data from a Kafka topic and write it to standard output." The other options are not valid Kafka CLI tools.
Reference:Apache Kafka Documentation > kafka-console-consumer.sh
NEW QUESTION # 78
Refer to the producer code below. It features a 'Callback' class with a method called 'onCompletion()'. When will the 'onCompletion()' method be Invoked?
producer.send(record, new MyCallback(record));
- A. When the producer batches the message
- B. When a consumer sends an acknowledgement to the producer
- C. When a broker sends an acknowledgement to the producer
- D. When the producer puts the message into its socket buffer
Answer: C
NEW QUESTION # 79
Your application is consuming from a topic with one consumer group.
The number of running consumers is equal to the number of partitions.
Application logs show that some consumers are leaving the consumer group during peak time, triggering a rebalance. You also notice that your application is processing many duplicates.
You need to stop consumers from leaving the consumer group.
What should you do?
- A. Increase session.timeout.ms property.
- B. Split consumers in different consumer groups.
- C. Add more consumer instances.
- D. Reduce max.poll.records property.
Answer: A
Explanation:
If the consumer fails to send heartbeats in time (due to being slow under load), it is considereddead, causing a rebalance. Increasing session.timeout.ms gives the consumermore time to process messages and send heartbeats, preventing premature removal.
FromKafka Docs > Heartbeats and Failures:
"If the consumer does not send a heartbeat in session.timeout.ms, it is considered dead and the group coordinator will trigger a rebalance." Reducing max.poll.records mightslow consumption further. More consumers won't help since each partition already has a dedicated consumer.
Reference:Apache Kafka Consumer Configs > session.timeout.ms
NEW QUESTION # 80
You have a Kafka Connect cluster with multiple connectors.
One connector is not working as expected.
How can you find logs related to that specific connector?
- A. Modify the log4j.properties file to add a dedicated log appender for the connector.
- B. Change the log level to DEBUG to have connector context information in logs.
- C. Make no change, there is no way to find logs other than by stopping all the other connectors.
- D. Modify the log4j.properties file to enable connector context.
Answer: A
Explanation:
To isolate logs for a specific connector, you can configurea separate logger and appenderin theConnect worker's log4j.propertiesfile, using the connector's name as the logging context.
FromKafka Connect Logging Docs:
"Kafka Connect loggers use hierarchical logger names. You can configure per-connector log levels and output files by extending log4j.properties."
* A and C change verbosity but don't separate logs.
* D is false; targeted logging is possible.
Reference:Kafka Connect > Logging and Debugging
NEW QUESTION # 81
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Confluent Certified Developer for Apache Kafka (CCDAK) Certification Examination is designed to test the knowledge and skills of developers working with Apache Kafka. The CCDAK exam is a vendor-neutral certification that is recognized globally and is considered as the industry standard for Kafka developers. Passing the exam is a great way to demonstrate your expertise in Kafka and can help you stand out in a competitive job market.
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