CCDAK Prüfungsmaterialien, CCDAK Prüfungsaufgaben

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Confluent CCDAK Exam Syllabus Topics:

Section	Weight	Objectives
Security	5-10%	- Encryption in Transit - Authorization (ACLs) - Securing Schema Registry - Authentication (SASL, SSL/TLS)
Schema Management	5-10%	- Schema Registration and Retrieval - Protobuf and JSON Schemas - Schema Registry Architecture - Avro Schemas - Schema Evolution and Compatibility
Developing with Kafka Producers	15-20%	- Exactly-Once Semantics - Error Handling and Retry Logic - Producer Interceptors - Serialization (JSON, Avro, Protobuf) - Idempotent Producers - Partitioning Strategies - Producer Configuration and Tuning
Kafka Streams	20-25%	- Exactly-Once Processing - State Stores and Processors - Streams Architecture and Topology - Interactive Queries - Transformation Operations (map, filter, join, aggregate) - Testing Kafka Streams Applications - Windowed Operations (Tumbling, Hopping, Session)
Kafka Fundamentals	10-15%	- Producer and Consumer Basics - Replication Factor and ISR - Log Segments and Retention - Topics, Partitions, and Offsets - Brokers and Clusters
Kafka Connect	10-15%	- Connector Configuration - Source and Sink Connectors - Connect Architecture (Workers, Connectors, Tasks) - Offset Management in Connect - Single Message Transformations (SMT) - Converters and Transforms
Monitoring and Operations	5-10%	- Kafka Metrics and Monitoring - Log Compaction - Disaster Recovery - Cross-region Replication - Consumer Lag and Health Checks
Installing and Configuring Kafka	5-10%	- Topic Creation and Configuration - Partition Assignment Strategies - ZooKeeper vs KRaft Mode - Broker Configuration Parameters
Developing with Kafka Consumers	15-20%	- Offset Management - Commit Strategies (Auto, Manual, Cooperative) - Consumer Interceptors - Consumer Groups and Rebalancing - Consumer Configuration and Tuning - Multi-threaded Consumers - Deserialization

>> CCDAK Prüfungsmaterialien <<

CCDAK Prüfungsaufgaben & CCDAK Fragen Beantworten

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Confluent Certified Developer for Apache Kafka Certification Examination CCDAK Prüfungsfragen mit Lösungen (Q13-Q18):

13. Frage
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?

A. KTable-KTable
B. KStream- KStream
C. KTable-KStream
D. KStream-GlobalKTable

Antwort: B

14. Frage
You are receiving orders from different customer in an "orders" topic with multiple partitions. Each message has the customer name as the key. There is a special customer named ABC that generates a lot of orders and you would like to reserve a partition exclusively for ABC. The rest of the message should be distributed among other partitions. How can this be achieved?

A. Create a custom partitioner
B. Add metadata to the producer record
C. All messages with the same key will go the same partition, but the same partition may have messages with different keys. It is not possible to reserve
D. Define a Kafka Broker routing rule

Antwort: A

Begründung:
A Custom Partitioner allows you to easily customise how the partition number gets computed from a source message.

15. Frage
(You are building real-time streaming applications using Kafka Streams.
Your application has a custom transformation.
You need to define custom processors in Kafka Streams.
Which tool should you use?)

A. Kafka Streams Custom Transformation Language
B. Processor API
C. TopologyTestDriver
D. Kafka Streams Domain Specific Language (DSL)

Antwort: B

Begründung:
The Apache Kafka Streams documentation clearly distinguishes between the Kafka Streams DSL and the Processor API. While the DSL is designed for common stream processing operations such as filtering, mapping, joining, and aggregations, it does not support fully custom processing logic.
For use cases that require custom transformations, fine-grained control over record processing, access to headers, timestamps, state stores, and punctuation, Kafka Streams provides the Processor API. This API allows developers to implement custom Processor, Transformer, or ValueTransformer classes and explicitly define the processing topology.
Option A (TopologyTestDriver) is a testing utility, not a development API. Option C (DSL) is higher-level and not suitable for advanced custom logic. Option D does not exist in Kafka.
Therefore, the correct and officially supported approach for defining custom processors in Kafka Streams is to use the Processor API.

16. Frage
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. json data
B. "test_binary" will receive
C. base64 encoded data, it will need to decode it
D. avro data
E. binary data

Antwort: E

Begründung:
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.

17. Frage
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 a consumer sends an acknowledgement to the producer
B. When a broker sends an acknowledgement to the producer
C. When the producer batches the message
D. When the producer puts the message into its socket buffer

Antwort: B

18. Frage
......

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