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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Apache Kafka Application Development | 28% | - Producer API: configuration, serialization, partitioning, error handling - Schema management: Confluent Schema Registry, compatibility rules, evolution - Consumer API: configuration, offset management, consumer groups, rebalancing - Transactional messaging and idempotence |
| Topic 2: Kafka Connect | 15% | - Data transformation and integration patterns - Source and sink connectors configuration - Connect architecture: workers, connectors, tasks, converters |
| Topic 3: Application Observability | 13% | - Logging and monitoring metrics - Tracing and performance tuning - Error diagnosis and troubleshooting |
| Topic 4: Apache Kafka Fundamentals | 23% | - Kafka architecture and data flow - Configuration basics and cluster setup - Core concepts: brokers, topics, partitions, replicas, offsets - Delivery semantics: at-most-once, at-least-once, exactly-once |
| Topic 5: Apache Kafka Streams | 12% | - Stream processing operations: map, filter, join, aggregation - Windowing and time semantics - Streams API concepts: topology, processors, state stores - Error handling and state management |
| Topic 6: Application Testing | 8% | - Integration testing and test clusters - Unit testing producers and consumers - Mocking and test utilities |
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NEW QUESTION # 57
In Java, Avro SpecificRecords classes are
Answer: B
Explanation:
SpecificRecord is created from generated record classes
NEW QUESTION # 58
If I want to send binary data through the REST proxy, it needs to be base64 encoded. Which component needs to encode the binary data into base 64?
Answer: B
Explanation:
The REST Proxy requires to receive data over REST that is already base64 encoded, hence it is the responsibility of the producer
NEW QUESTION # 59
A consumer application needs to use an "at most once" delivery semantic.
What is the best consumer configuration lo avoid duplicate messages being read?
Answer: A
NEW QUESTION # 60
What is a consequence of increasing the number of partitions in an existing Kafka topic?
Answer: D
Explanation:
Increasing partitions increases parallelism, but also means:
Consumers in a group may have to handle more partitions, especially if the number of consumers is lower than the number of partitions.
This can result in increased lag, especially under high load.
From Kafka Topic Management Docs:
"Increasing the number of partitions increases consumer work, and if consumers can't keep up, lag can accumulate." A is false: existing data is not redistributed.
B is false: records with the same key always map to the same partition based on hash.
D is not directly impacted by the partition count.
Reference: Kafka Topic Management > Adding Partitions
NEW QUESTION # 61
(Which configuration determines the maximum number of records a consumer can poll in a single call to poll()?)
Answer: C
Explanation:
The Apache Kafka consumer configuration max.poll.records explicitly controls the maximum number of records returned in a single call to Consumer.poll(). This is clearly documented in the official Kafka consumer configuration reference. It allows developers to limit the size of the batch returned to the application, helping to control processing latency, memory usage, and CPU load.
This setting does not affect how much data the broker sends to the consumer; instead, it limits how many records the consumer returns to the application layer from the fetched data. This makes it especially useful for applications where processing each record is expensive and needs predictable batch sizes.
Option B (max.records.consumer) does not exist in Kafka. Option C (fetch.max.records) is also invalid; Kafka uses fetch.max.bytes and related byte-based configurations, not record-count limits, at the fetch level. Option D (max.poll.records.interval) is not a valid Kafka configuration; the correct related setting is max.poll.interval.
ms, which controls liveness, not batch size.
Thus, max.poll.records is the correct and officially documented configuration.
NEW QUESTION # 62
......
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