Pass Authentic Confluent CCDAK with Free Practice Tests and Exam Dumps [Q54-Q74]

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Pass Authentic Confluent CCDAK with Free Practice Tests and Exam Dumps

New CCDAK Exam Questions Real Confluent Dumps

The CCDAK certification exam consists of a series of multiple-choice questions that assess the candidate’s knowledge in various aspects of Kafka development. CCDAK exam covers topics such as Kafka architecture, Kafka APIs, Kafka Connect, Kafka Streams, and Kafka Security. Candidates must pass the exam with a minimum score of 70% to earn the CCDAK certification. Confluent Certified Developer for Apache Kafka Certification Examination certification is valid for two years and can be renewed by passing the recertification exam. The CCDAK certification program is an excellent way for developers to demonstrate their Kafka development skills, increase their marketability, and advance their careers in the field of big data and streaming technologies.

Confluent CCDAK certification exam covers a wide range of topics related to Kafka, including Kafka architecture, Kafka clusters, Kafka streams, Kafka security, and Kafka monitoring. Confluent Certified Developer for Apache Kafka Certification Examination certification exam is designed for developers who have experience with Kafka and want to validate their skills and knowledge in the field. CCDAK exam is designed to test the understanding and expertise of developers in Kafka and related technologies.

NEW QUESTION 54
What is the function of Kafka Connect Converters?

Make simple updates to data as it is written to and read from Kafka topics.

Serialize data written to and deserialize data read from Kafka topics.

Convert data from the structure used by the source system to the standard Connect structure.

Isolate the producer and consumer work from the source and sink connectors.

NEW QUESTION 55
To allow consumers in a group to resume at the previously committed offset, I need to set the proper value for…

value.deserializer

auto.offset.resets

group.id

enable.auto.commit

Setting a group.id that’s consistent across restarts will allow your consumers part of the same group to resume reading from where offsets were last committed for that group

NEW QUESTION 56
What will happen if a producer tries to send messages to a topic that does not exist in the Kafka cluster?

The messages will be sent to a topic called unnamed_topic_1.

Messages will only be sent if auto topic creation is enabled.

The brokers will hold the messages until a user creates a topic for the messages.

The Producer “send()” call will block indefinitely.

NEW QUESTION 57
How will you read all the messages from a topic in your KSQL query?

KSQL reads from the beginning of a topic, by default.

KSQL reads from the end of a topic. This cannot be changed.

Use KSQL CLI to set auto.offset.reset property to earliest

Consumers can set auto.offset.reset property to earliest to start consuming from beginning. For KSQL, SET ‘auto.offset.reset’=’earliest’;

NEW QUESTION 58
An application is writing AVRO messages using Schema Registry to topic t1. During this process, the Schema Registry becomes unavailable for a few seconds.
What is the expected impact to the application?

The application may not have any impact, unless it is writing messages with a new Schema Definition.

All messages within that time will receive an error.

Since messages are cached by the producer, the application will only get an error if the producer is sending the batch at that time.

Since the broker will eventually replicate the message Schema, there will not be an error.

NEW QUESTION 59
A kafka topic has a replication factor of 3 and min.insync.replicas setting of 2. How many brokers can go down before a producer with acks=1 can’t produce?

min.insync.replicas does not impact producers when acks=1 (only when acks=all)

NEW QUESTION 60
Which actions will trigger partition rebalance for a consumer group? (select three)

Increase partitions of a topic

Remove a broker from the cluster

Add a new consumer to consumer group

A consumer in a consumer group shuts down

Add a broker to the cluster

Rebalance occurs when a new consumer is added, removed or consumer dies or paritions increased.

NEW QUESTION 61
What happens if you write the following code in your producer? producer.send(producerRecord).get()

Compression will be increased

Throughput will be decreased

It will force all brokers in Kafka to acknowledge the producerRecord

Batching will be increased

Using Future.get() to wait for a reply from Kafka will limit throughput.

NEW QUESTION 62
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?

Modify the log4j.properties file to enable connector context.

Modify the log4j.properties file to add a dedicated log appender for the connector.

Change the log level to DEBUG to have connector context information in logs.

Make no change, there is no way to find logs other than by stopping all the other connectors.

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 63
What exceptions may be caught by the following producer? (select two)
ProducerRecord<String, String> record =
new ProducerRecord<>(“topic1”, “key1”, “value1”);
try {
producer.send(record);
} catch (Exception e) {
e.printStackTrace();
}

BrokerNotAvailableException

SerializationException

InvalidPartitionsException

BufferExhaustedException

These are the client side exceptions that may be encountered before message is sent to the broker, and before a future is returned by the .send() method.

NEW QUESTION 64
Which SMTs is used to change the data type?

org.apache.kafka.connect.transforms.Cast

org.apache.kafka.connect.transforms.Flatten

org.apache.kafka.connect.transforms.TimestampRouter

io.confluent.connect.transforms.TombstoneHandler

NEW QUESTION 65
Which of the following Kafka Streams operators are stateless? (select all that apply)

map

filter

flatmap

branch

groupBy

aggregate

Seehttps://kafka.apache.org/20/documentation/streams/developer-guide/dsl-api.html#stateless-transformations

NEW QUESTION 66
The rule “same key goes to the same partition” is true unless…

the number of producer changes

the number of kafka broker changes

the number of partition changes

the replication factor changes

Increasing the number of partition causes new messages keys to get hashed differently, and breaks the guarantee “same keys goes to the same partition”. Kafka logs are immutable and the previous messages are not re-shuffled.

NEW QUESTION 67
What is accomplished by producing data to a topic with a message key?

Messages with the same key are routed to a deterministically selected partition, enabling order guarantees within that partition.

Kafka brokers allow you to add more partitions to a given topic, without impacting the data flow for existing keys.

It provides a mechanism for encrypting messages at the partition level to ensure secure data transmission.

Consumers can filter messages in real time based on the message key without processing unrelated messages.

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 68
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?

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.

NEW QUESTION 69
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));

When a consumer sends an acknowledgement to the producer

When the producer puts the message into its socket buffer

When the producer batches the message

When a broker sends an acknowledgement to the producer

NEW QUESTION 70
What’s is true about Kafka brokers and clients from version 0.10.2 onwards?

Clients and brokers must have the exact same version to be able to communicate

A newer client can talk to a newer broker, but an older client cannot talk to a newer broker

A newer client can talk to a newer broker, and an older client can talk to a newer broker

A newer client can’t talk to a newer broker, but an older client can talk to a newer broker

Kafka’s new bidirectional client compatibility introduced in 0.10.2 allows this. Read more herehttps://www.confluent.io/blog/upgrading-apache-kafka-clients-just-got-easier/

NEW QUESTION 71
To transform data from a Kafka topic to another one, I should use

Kafka Connect Sink

Kafka Connect Source

Consumer + Producer

Kafka Streams

Kafka Streams is a library for building streaming applications, specifically applications that transform input Kafka topics into output Kafka topics

NEW QUESTION 72
Kafka is configured with following parameters – log.retention.hours = 168 log.retention.minutes = 168 log.
retention.ms = 168 How long will the messages be retained for?

Broker will not start due to bad configuration

168 ms

168 hours

168 minutes

If more than one similar config is specified, the smaller unit size will take precedence.

NEW QUESTION 73
In stream processing applications, handling out-of-order data can be a challenge which could affect the implemented business logic.
Which type of Join handles out-of-order records?

KStream- KStream

KTable-KStream

KTable-KTable

KStream-GlobalKTable

NEW QUESTION 74
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?

Sliding

Tumbling

Hopping

Session

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