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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Kafka Streams | 20-25% | - Transformation Operations (map, filter, join, aggregate) - Windowed Operations (Tumbling, Hopping, Session) - Testing Kafka Streams Applications - Streams Architecture and Topology - Exactly-Once Processing - State Stores and Processors - Interactive Queries |
| Topic 2: Security | 5-10% | - Authentication (SASL, SSL/TLS) - Securing Schema Registry - Authorization (ACLs) - Encryption in Transit |
| Topic 3: Developing with Kafka Consumers | 15-20% | - Consumer Interceptors - Consumer Groups and Rebalancing - Consumer Configuration and Tuning - Commit Strategies (Auto, Manual, Cooperative) - Deserialization - Multi-threaded Consumers - Offset Management |
| Topic 4: Schema Management | 5-10% | - Schema Registration and Retrieval - Schema Registry Architecture - Avro Schemas - Protobuf and JSON Schemas - Schema Evolution and Compatibility |
| Topic 5: Monitoring and Operations | 5-10% | - Disaster Recovery - Log Compaction - Cross-region Replication - Consumer Lag and Health Checks - Kafka Metrics and Monitoring |
| Topic 6: Kafka Connect | 10-15% | - Single Message Transformations (SMT) - Source and Sink Connectors - Offset Management in Connect - Converters and Transforms - Connector Configuration - Connect Architecture (Workers, Connectors, Tasks) |
| Topic 7: Developing with Kafka Producers | 15-20% | - Producer Interceptors - Error Handling and Retry Logic - Producer Configuration and Tuning - Serialization (JSON, Avro, Protobuf) - Partitioning Strategies - Exactly-Once Semantics - Idempotent Producers |
| Topic 8: Installing and Configuring Kafka | 5-10% | - Topic Creation and Configuration - Partition Assignment Strategies - ZooKeeper vs KRaft Mode - Broker Configuration Parameters |
| Topic 9: Kafka Fundamentals | 10-15% | - Topics, Partitions, and Offsets - Log Segments and Retention - Replication Factor and ISR - Producer and Consumer Basics - Brokers and Clusters |
IT업계에 종사하고 계신 분은Confluent CCDAK 시험을 패스하여 자격증을 취득하려고 검색하다 저희 블로그를 보게 되시고 저희 사이트까지 방문하게 될것입니다. 방문하는 순간 Confluent CCDAK시험에 대한 두려움이 사라질것입니다. 완벽한 구매후 서비스까지 겸비하고 있어 자격증을 취득하는데서의 믿음직스러운 동반자로 되어드릴게요.
질문 # 25
You need to correctly join data from two Kafka topics.
Which two scenarios will allow for co-partitioning?
(Select two.)
정답：A,D
설명：
For Kafka Streams to performjoins, topics must beco-partitioned, meaning:
* They have thesame number of partitions(A), and
* They use thesame partitioning strategy(typically by key) (B)
FromKafka Streams Documentation > Joins:
"Co-partitioning is required for Kafka Streams joins. Topics must have the same number of partitions and use the same key-based partitioner." Value schema (C) and retention time (D) have no bearing on join compatibility.
Reference:Kafka Streams Concepts > Joins and Co-Partitioning
질문 # 26
You are creating a Kafka Streams application to process retail data.
Match the input data streams with the appropriate Kafka Streams object.
정답：
설명：
Explanation:
Table # Product, Customers
Stream # Orders_Placed, Shipment_Of_Orders
Tables represent slowly changing, reference data - e.g., customer profiles, product info.
Streams represent real-time event data - e.g., orders placed, shipments processed.
From Kafka Streams Documentation:
"Use KTable for reference datasets, and KStream for event-based processing such as orders or logs." Reference: Kafka Streams API Concepts
질문 # 27
You have a Kafka client application that has real-time processing requirements.
Which Kafka metric should you monitor?
정답：D
설명：
For real-time applications, the key metric to monitor is consumer lag - the difference between the latest offset on the broker and the last committed offset by the consumer.
From Kafka Monitoring Guide:
"Consumer lag is the most important metric for real-time applications. It tells you how far behind the consumer is from the latest data in Kafka." B relates to replication, not client responsiveness.
C tracks group membership stability, not data delay.
D tracks input throughput, not processing latency.
Reference: Kafka Operations > Monitoring > Consumer Lag
질문 # 28
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?
정답：D
질문 # 29
What are three built-in abstractions in the Kafka Streams DSL?
(Select three.)
정답：B,D,E
설명：
The Kafka Streams DSL provides three primary abstractions:
KStream: Represents an unbounded stream of records (append-only).
KTable: Represents a changelog stream where each key has the latest value (like a materialized view).
GlobalKTable: A fully replicated version of KTable across all stream instances, enabling global joins.
From Kafka Streams Developer Guide:
"Kafka Streams provides three core abstractions: KStream, KTable, and GlobalKTable, each serving different purposes in stream processing and joins." GlobalKStream and StreamTable do not exist in Kafka's API.
Reference: Kafka Streams Concepts > KStream, KTable, GlobalKTable
질문 # 30
......
Confluent CCDAK인증시험에 응시하고 싶으시다면 좋은 학습자료와 학습 가이드가 필요합니다.Confluent CCDAK시험은 it업계에서도 아주 중요한 인증입니다. 시험패스를 원하신다면 충분한 시험준비는 필수입니다.
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