Latest PDF of CCDAK: Confluent Certified Developer for Apache Kafka

Confluent Certified Developer for Apache Kafka Practice Test

CCDAK test Format | Course Contents | Course Outline | test Syllabus | test Objectives

Title: Confluent Certified Developer for Apache Kafka (CCDAK)

Test Detail:
The CCDAK (Confluent Certified Developer for Apache Kafka) is a professional certification offered by Confluent, the company behind Apache Kafka. It validates the knowledge and skills of developers in designing, developing, and implementing Apache Kafka-based solutions. The certification demonstrates expertise in building real-time streaming applications using Kafka.

Course Outline:
The CCDAK certification program covers a comprehensive range of syllabus related to Apache Kafka development. The course provides participants with a solid foundation in Kafka concepts, architecture, and application development. The following is a general outline of the key areas covered in the CCDAK certification program:

1. Introduction to Apache Kafka:
- Kafka architecture and components
- Kafka topics, partitions, and offsets
- Kafka brokers and clusters
- Producers and consumers
- Kafka Connect and Kafka Streams

2. Kafka Core APIs:
- Producer API
- Consumer API
- Kafka Streams API
- Admin API
- Schema Registry API

3. Kafka Data Serialization:
- Avro schema evolution and compatibility
- JSON and binary data serialization
- Kafka message headers
- Serializers and deserializers
- Apache Kafka and Confluent schema registry

4. Kafka Streams Processing:
- Stream processing fundamentals
- Stream processing topologies
- Stateful and stateless operations
- Windowed operations
- Interactive queries

5. Kafka Connect and Integration:
- Kafka Connect architecture
- Connectors and transformations
- Sinks and sources
- Connect REST API
- Integration with external systems

Exam Objectives:
The CCDAK certification test assesses candidates' understanding of Apache Kafka concepts, programming techniques, and best practices. The test objectives include, but are not limited to:

1. Demonstrating knowledge of Kafka architecture and components.
2. Implementing Kafka producers and consumers using the core APIs.
3. Developing real-time stream processing applications with Kafka Streams.
4. Configuring and managing Kafka Connect for data integration.
5. Understanding data serialization and compatibility using Avro and JSON.
6. Applying best practices for scalability, fault tolerance, and performance.
7. Troubleshooting and debugging Kafka applications.

Syllabus:
The CCDAK certification program typically spans a multi-day training course or self-paced online learning modules. The syllabus provides a breakdown of the syllabus covered throughout the course, including specific learning objectives and milestones. The syllabus may include the following components:

- Introduction to Apache Kafka
- Kafka Core APIs and Application Development
- Kafka Data Serialization and Schema Registry
- Kafka Streams Processing and Real-time Analytics
- Kafka Connect and Integration with External Systems
- Best Practices and Advanced Topics
- test Preparation and Mock Tests
- Final CCDAK Certification Exam

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killexams.com Confluent CCDAK
Confluent Certified Developer for Apache Kafka
https://killexams.com/pass4sure/exam-detail/CCDAK
Question: 354
Which of the following is NOT a valid Kafka Connect connector type?
nk Connector rocessor Connector ransform Connector
wer: C
anation: "Processor Connector" is not a valid Kafka Connect connecto The valid connector types are Source Connector (for importing data i a), Sink Connector (for exporting data from Kafka), and Transform nector (for modifying or transforming data during the import or export ess).
stion: 355
ch of the following is a benefit of using Apache Kafka for real-time d ming?
Source Connector
Si
P
T
Ans
Expl r
type. nto
Kafk Con proc
Que
Whi ata
strea
1. High-latency message delivery
2. Centralized message storage and processing
3. Limited scalability and throughput
4. Inability to handle large volumes of data
5. Fault-tolerance and high availability
Answer: E
stion: 356
ch of the following is NOT a valid deployment option for Kafka? n-premises deployment
loud deployment (e.g., AWS, Azure) ontainerized deployment (e.g., Docker) obile deployment (e.g., Android, iOS)
wer: D
anation: Mobile deployment (e.g., Android, iOS) is not a valid deploy n for Kafka. Kafka is typically deployed in server or cloud environme ndle high-throughput and real-time data streaming. It is commonly oyed onservers in on-premises data centers or in the cloud, such as A
Explanation: One of the benefits of using Apache Kafka for real-time data streaming is its fault-tolerance and high availability. Kafka is designed to provide durability, fault tolerance, and high availability of data streams. It can handle large volumes of data and offers high scalability and throughput. Kafka also allows for centralized message storage and processing, enabling real-time processing of data from multiple sources.
Que
Whi
1. O
2. C
3. C
4. M
Ans
Expl ment
optio nts
to ha
depl WS
(Amazon Web Services) or Azure. Kafka can also be containerized using technologies like Docker and deployed in container orchestration platforms like Kubernetes. However, deploying Kafka on mobile platforms like Android or iOS is not a typical use case. Kafka is designed for server-side data processing and messaging, and it is not optimized for mobile devices.
Question: 357
Which of the following is a feature of Kafka Streams?
1. It provides a distributed messaging system for real-time data processing.
enables automatic scaling of Kafka clusters based on load. wer: B
anation: Kafka Streams supports exactly-once processing semantics f m processing. This means that when processing data streams using Ka ms, each record is processed exactly once, ensuring data integrity and istency. This is achieved through a combination of Kafka's transaction aging and state management features in Kafka Streams.
stion: 358
When designing a Kafka consumer application, what is the purpose of sett uto.offset.reset property?
It supports exactly-once processing semantics for stream processing.
2. It
Ans
Expl or
strea fka
Strea
cons al
mess
Que
the a
ing
1. To control the maximum number of messages to be fetched per poll.
2. To specify the subject to consume messages from.
3. To determine the behavior when there is no initial offset in Kafka or if the current offset does not exist.
4. To configure the maximum amount of time the consumer will wait for new messages.
Answer: C
Explanation: The auto.offset.reset property is used to determine the behavior when there is no initial offset in Kafka or if the current offset does not exist. It specifies whether the consumer should automatically reset the offset to the earliest or latest available offset in such cases.
stion: 359
is the role of a Kafka producer?
consume messages from Kafka syllabus and process them. store and manage the data in Kafka topics.
replicate Kafka subject data across multiple brokers. publish messages to Kafka topics.
wer: D
anation: The role of a Kafka producer is to publish messages to Kafka
s. Producers are responsible for sending messages to Kafka brokers, w istribute the messages to the appropriate partitions of the specified t
ucers can be used to publish data in real-time or batch mode to Kafka er processing or consumption.
Que
What
1. To
2. To
3. To
4. To
Ans Expl
topic hich
then d opics.
Prod for
furth
Question: 360
Which of the following is a valid way to configure Kafka producer retries?
1. Using the retries property in the producer configuration
2. Using the retry.count property in the producer configuration
3. Using the producer.retries property in the producer configuration
4. Using the producer.retry.count property in the producer configuration Answer: A
es that the producer will attempt in case of transient failures.
stion: 361
ch of the following is NOT a valid approach for Kafka cluster scalabil ncreasing the number of brokers
creasing the number of partitions per subject creasing the replication factor for syllabus ncreasing the retention period for messages
wer: D
anation: Increasing the retention period for messages is not a valid oach for Kafka cluster scalability. The retention period determines ho
essages are retained within Kafka, but it does not directly impact th
Explanation: Kafka producer retries can be configured using the retries property in the producer configuration. This property specifies the number of retri
Que
Whi ity?
1. I
2. In
3. In
4. I
Ans Expl
appr w
long m e
scalability of the cluster. Valid approaches for scalability include increasing the number of brokers, partitions, and replication factor.
Question: 362
Which of the following is NOT a core component of Apache Kafka?
1. ZooKeeper
2. Kafka Connect
3. Kafka Streams
anation: ZooKeeper, Kafka Connect, and Kafka Streams are all core ponents of Apache Kafka. ZooKeeper is used for coordination, hronization, and configuration management in Kafka. Kafka Connect ework for connecting Kafka with external systems. Kafka Streams is ry for building stream processing applications with Kafka. However, ka Manager" is not a core component of Kafka. It is a third-party tool anaging and monitoring Kafka clusters.
stion: 363
ch of the following is true about Kafka replication?
afka replication ensures that each message in a subject is stored on mult ers for fault tolerance.
afka replication is only applicable to log-compacted topics.
Kafka Manager Answer: D
Expl com
sync is a
fram a
libra
"Kaf used
for m
Que
Whi
1. K iple
brok
2. K
3. Kafka replication allows data to be synchronized between Kafka and external systems.
4. Kafka replication enables compression and encryption of messages in Kafka. Answer: A
Explanation: Kafka replication ensures fault tolerance by storing multiple
copies of each message in a subject across different Kafka brokers. Each subject partition can have multiple replicas, and Kafka automatically handles replication and leader election to ensure high availability and durability of data.
Question: 364
is Kafka log compaction?
process that compresses the Kafka log files to save disk space. process that removes duplicate messages from Kafka topics.
process that deletes old messages from Kafka syllabus to free up disk s process that retains only the latest value for each key in a Kafka topic
wer: D
anation: Kafka log compaction is a process that retains only the latest ach key in a Kafka topic. It ensures that the log maintains a compact sentation of the data, removingany duplicate or obsolete messages. L paction is useful when the retention of the full message history is not red, and only the latest state for each key is needed.
stion: 365
What
1. A
2. A
3. A pace.
4. A .
Ans
Expl value
for e
repre og
com requi
Que
What is the significance of the acks configuration parameter in the Kafka producer?
1. It determines the number of acknowledgments the leader broker must receive before considering a message as committed.
2. It defines the number of replicas that must acknowledge the message before
considering it as committed.
3. It specifies the number of retries the producer will attempt in case of failures before giving up.
4. It sets the maximum size of messages that the producer can send to the broker.
anation: The acks configuration parameter in the Kafka producer mines the number of acknowledgments the leader broker must receiv re considering a message as committed. It can be set to "all" (which m
sync replicas must acknowledge), "1" (which means only the leader owledge), or a specific number of acknowledgments.
stion: 366
ch of the following is NOT a valid method for handling Kafka messag lization?
SON
vro rotobuf ML
Answer: A Expl
deter e
befo eans
all in- must
ackn
Que
Whi e
seria
1. J
2. A
3. P
4. X
Answer: D
Explanation: "XML" is not a valid method for handling Kafka message serialization. Kafka supports various serialization formats such as JSON, Avro, and Protobuf, but not XML.
Which of the following is the correct command to create a new consumer group in Apache Kafka?
afka-consumer-groups.sh --create --group my_group
afka-consumer-groups.sh --bootstrap-server localhost:2181 --create -- group
afka-consumer-groups.sh --group my_group --create wer: A
anation: The correct command to create a new consumer group in Ap a is "kafka-consumer-groups.sh --bootstrap-server localhost:9092 --cr up my_group". This command creates a new consumer group with th fied group name. The "--bootstrap-server" option specifies the Kafka strap server, and the "--group" option specifies the consumer group na ther options mentioned either have incorrect parameters or do not in ecessary bootstrap server information.
stion: 368
kafka-consumer-groups.sh --bootstrap-server localhost:9092 --create --group my_group
k
k group
my_
k Ans
Expl ache
Kafk eate
--gro e
speci
boot me.
The o clude
the n
Que
What is the purpose of a Kafka producer in Apache Kafka?
1. To consume messages from Kafka topics
2. To manage the replication of data across Kafka brokers
3. To provide fault tolerance by distributing the load across multiple consumers
4. To publish messages to Kafka topics
age. They play a crucial role in the data flow of Kafka by publishing ages for consumption by consumers.
stion: 369
is the purpose of the Kafka Connect Transformer? convert Kafka messages from one subject to another
transform the data format of Kafka messages
perform real-time stream processing within a Kafka cluster manage and monitor the health of Kafka Connect connectors
wer: B
anation: The Kafka Connect Transformer is used to transform the data at of Kafka messages during the import or export process. It allows fo
Explanation: The purpose of a Kafka producer in Apache Kafka is to publish messages to Kafka topics. Producers are responsible for creating and sending messages to Kafka brokers, which then distribute the messages to the appropriate partitions of the topics. Producers can specify the subject and partition to which a message should be sent, as well as the key and value of the mess new
mess
Que
What
1. To
2. To
3. To
4. To
Ans Expl
form r the
modification, enrichment, or restructuring of the data being transferred between Kafka and external systems by applying custom transformations to the messages.

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