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| Section | Weight | Objectives |
|---|---|---|
| Apache Kafka Streams | 12% | - Streams API concepts: topology, processors, state stores - Stream processing operations: map, filter, join, aggregation - Error handling and state management - Windowing and time semantics |
| Kafka Connect | 15% | - Source and sink connectors configuration - Connect architecture: workers, connectors, tasks, converters - Data transformation and integration patterns |
| Apache Kafka Fundamentals | 23% | - Core concepts: brokers, topics, partitions, replicas, offsets - Configuration basics and cluster setup - Delivery semantics: at-most-once, at-least-once, exactly-once - Kafka architecture and data flow |
| Application Observability | 13% | - Error diagnosis and troubleshooting - Tracing and performance tuning - Logging and monitoring metrics |
| Application Testing | 8% | - Unit testing producers and consumers - Mocking and test utilities - Integration testing and test clusters |
| Apache Kafka Application Development | 28% | - Consumer API: configuration, offset management, consumer groups, rebalancing - Transactional messaging and idempotence - Producer API: configuration, serialization, partitioning, error handling - Schema management: Confluent Schema Registry, compatibility rules, evolution |
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NEW QUESTION # 16
Which of the following Kafka Streams operators are stateful? (select all that apply)
Answer: C,D,E,F
Explanation:
Seehttps://kafka.apache.org/20/documentation/streams/developer-guide/dsl-api.html#stateful-transformations
NEW QUESTION # 17
Which configuration determines how many bytes of data are collected before sending messages to the Kafka broker?
Answer: A
Explanation:
The batch.size config sets the maximum number of bytes to batch per partition before sending. This allows Kafka producers to amortize I/O and improve throughput.
From Kafka Producer Configuration Docs:
"batch.size is the maximum amount of data per partition the producer will batch before sending." buffer.memory sets total memory for the producer, not per-batch.
send.buffer.bytes is a TCP socket buffer, not a Kafka config.
max.block.ms controls blocking time, not size.
Reference: Kafka Producer Configs > batch.size
NEW QUESTION # 18
You have a topic with four partitions. The application reads from it using two consumers in a single consumer group.
Processing is CPU-bound, and lag is increasing.
What should you do?
Answer: C
Explanation:
If the application isCPU-boundandlagging, addingmore consumersto the group will allow betterparallel processing, especially since the topic has4 partitions, allowing up to 4 active consumers.
FromKafka Consumer Group Docs:
"Kafka achieves parallelism by distributing partitions across consumers in a group. Adding consumers helps reduce lag if partitions are underutilized."
* B may help but requires repartitioning and coordination.
* C or D affects how much data is polled, not how fast it's processed.
Reference:Kafka Consumer Concepts > Parallelism and Scaling
NEW QUESTION # 19
Your application is consuming from a topic configured with a deserializer.
It needs to be resilient to badly formatted records ("poison pills"). You surround the poll() call with a try/catch for RecordDeserializationException.
You need to log the bad record, skip it, and continue processing.
Which action should you take in the catch block?
Answer: D
Explanation:
To skip a corrupted record and avoid failing the application, you must seek past the failed offset manually using consumer.seek(). This allows the application to resume consumption from the next offset.
From Kafka Consumer Error Handling Docs:
"On deserialization failure, you can catch RecordDeserializationException, log the error, and call seek() to the next offset to skip the bad record." A does not prevent re-processing the bad record.
C is invalid; there's no skip() method in the Kafka consumer API.
D results in service interruption - not ideal for resiliency.
Reference: Kafka Consumer API - Exception Handling and seek()
NEW QUESTION # 20
You are experiencing low throughput from a Java producer.
Metrics show low I/O thread ratio and low I/O thread wait ratio.
What is the most likely cause of the slow producer performance?
Answer: A
Explanation:
Low I/O thread activity with blocked throughput often indicates thatproducer callbacks are consuming too much time, causing the sender thread to block while waiting for onCompletion() to finish.
FromKafka Producer Performance Guide:
"Expensive logic in callbacks (e.g., I/O or complex computation) can block the sender thread, reducing throughput."
* Compression (A) may slightly impact CPU but not I/O thread usage.
* Large batches (B) improve throughput if managed correctly.
* A Layer 2 network issue (C) would lead to packet loss, not specifically low callback metrics.
Reference:Kafka Producer Metrics and Performance Tuning
NEW QUESTION # 21
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