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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Application Testing | 8% | - Mocking and test utilities - Integration testing and test clusters - Unit testing producers and consumers |
| Topic 2: Apache Kafka Streams | 12% | - Windowing and time semantics - Error handling and state management - Stream processing operations: map, filter, join, aggregation - Streams API concepts: topology, processors, state stores |
| Topic 3: Kafka Connect | 15% | - Data transformation and integration patterns - Source and sink connectors configuration - Connect architecture: workers, connectors, tasks, converters |
| Topic 4: Application Observability | 13% | - Error diagnosis and troubleshooting - Logging and monitoring metrics - Tracing and performance tuning |
| Topic 5: Apache Kafka Application Development | 28% | - Schema management: Confluent Schema Registry, compatibility rules, evolution - Consumer API: configuration, offset management, consumer groups, rebalancing - Producer API: configuration, serialization, partitioning, error handling - Transactional messaging and idempotence |
| Topic 6: Apache Kafka Fundamentals | 23% | - Configuration basics and cluster setup - Delivery semantics: at-most-once, at-least-once, exactly-once - Core concepts: brokers, topics, partitions, replicas, offsets - Kafka architecture and data flow |
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問題 #36
You are creating a Kafka Streams application to process retail data.
Match the input data streams with the appropriate Kafka Streams object.
答案：
解題說明：
Explanation:
* Table# Product, Customers
* Stream# Orders_Placed, Shipment_Of_Orders
* Tablesrepresent slowly changing,reference data- e.g., customer profiles, product info.
* Streamsrepresentreal-time event data- e.g., orders placed, shipments processed.
FromKafka Streams Documentation:
"Use KTable for reference datasets, and KStream for event-based processing such as orders or logs." Reference:Kafka Streams API Concepts
問題 #37
You create a topic with five partitions.
What can be assumed about messages read from that topic by a single consumer group?
答案：C
問題 #38
You are running a Kafka Streams application in a Docker container managed by Kubernetes, and upon application restart, it takes a long time for the docker container to replicate the state and get back to processing the dat a. How can you improve dramatically the application restart?
答案：B
解題說明：
Although any Kafka Streams application is stateless as the state is stored in Kafka, it can take a while and lots of resources to recover the state from Kafka. In order to speed up recovery, it is advised to store the Kafka Streams state on a persistent volume, so that only the missing part of the state needs to be recovered.
問題 #39
Which of the following event processing application is stateless? (select two)
答案：A,B
解題說明：
Stateless means processing of each message depends only on the message, so converting from JSON to Avro or filtering a stream are both stateless operations
問題 #40
What's is true about Kafka brokers and clients from version 0.10.2 onwards?
答案：C
解題說明：
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/
問題 #41
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