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| Section | Weight | Objectives |
|---|---|---|
| Kafka Connect | 15% | - Connect architecture: workers, connectors, tasks, converters - Source and sink connectors configuration - Data transformation and integration patterns |
| Application Testing | 8% | - Unit testing producers and consumers - Mocking and test utilities - Integration testing and test clusters |
| Apache Kafka Fundamentals | 23% | - Kafka architecture and data flow - Configuration basics and cluster setup - Delivery semantics: at-most-once, at-least-once, exactly-once - Core concepts: brokers, topics, partitions, replicas, offsets |
| Apache Kafka Streams | 12% | - Streams API concepts: topology, processors, state stores - Windowing and time semantics - Error handling and state management - Stream processing operations: map, filter, join, aggregation |
| Apache Kafka Application Development | 28% | - Consumer API: configuration, offset management, consumer groups, rebalancing - Transactional messaging and idempotence - Schema management: Confluent Schema Registry, compatibility rules, evolution - Producer API: configuration, serialization, partitioning, error handling |
| Application Observability | 13% | - Error diagnosis and troubleshooting - Tracing and performance tuning - Logging and monitoring metrics |
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NEW QUESTION # 77
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?
Answer: A
Explanation:
A Custom Partitioner allows you to easily customise how the partition number gets computed from a source message.
NEW QUESTION # 78
What are three built-in abstractions in the Kafka Streams DSL?
(Select three.)
Answer: A,B,C
Explanation:
TheKafka Streams DSLprovides 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.
FromKafka 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 donot existin Kafka's API.
Reference:Kafka Streams Concepts > KStream, KTable, GlobalKTable
NEW QUESTION # 79
You have a Kafka Connect cluster with multiple connectors.
One connector is not working as expected.
How can you find logs related to that specific connector?
Answer: B
Explanation:
To isolate logs for a specific connector, you can configurea separate logger and appenderin theConnect worker's log4j.propertiesfile, using the connector's name as the logging context.
FromKafka Connect Logging Docs:
"Kafka Connect loggers use hierarchical logger names. You can configure per-connector log levels and output files by extending log4j.properties."
* A and C change verbosity but don't separate logs.
* D is false; targeted logging is possible.
Reference:Kafka Connect > Logging and Debugging
NEW QUESTION # 80
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: D
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 # 81
Which two statements are correct when assigning partitions to the consumers in a consumer group using the assign() API?
(Select two.)
Answer: A,D
Explanation:
Using assign() gives manual control over partition assignment. In this mode:
B is true: The application chooses partitions directly without involving the group coordinator.
C is true: Since no group coordination occurs, no rebalancing happens if a consumer dies.
From Kafka Consumer API Docs:
"assign() manually assigns a list of partitions to this consumer. No coordination takes place and no rebalancing is triggered." A is false: subscribe() and assign() are mutually exclusive.
D is incorrect: Partitions don't need to be equal across topics.
Reference: Kafka Consumer Java API > assign() and subscribe()
NEW QUESTION # 82
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