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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Apache Kafka Application Development | 28% | - Consumer API: configuration, offset management, consumer groups, rebalancing - Producer API: configuration, serialization, partitioning, error handling - Schema management: Confluent Schema Registry, compatibility rules, evolution - Transactional messaging and idempotence |
| Topic 2: Apache Kafka Fundamentals | 23% | - Core concepts: brokers, topics, partitions, replicas, offsets - Configuration basics and cluster setup - Kafka architecture and data flow - Delivery semantics: at-most-once, at-least-once, exactly-once |
| Topic 3: Apache Kafka Streams | 12% | - Error handling and state management - Windowing and time semantics - Stream processing operations: map, filter, join, aggregation - Streams API concepts: topology, processors, state stores |
| Topic 4: Application Testing | 8% | - Mocking and test utilities - Unit testing producers and consumers - Integration testing and test clusters |
| Topic 5: Kafka Connect | 15% | - Data transformation and integration patterns - Source and sink connectors configuration - Connect architecture: workers, connectors, tasks, converters |
| Topic 6: Application Observability | 13% | - Logging and monitoring metrics - Tracing and performance tuning - Error diagnosis and troubleshooting |
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NEW QUESTION # 66
Which KSQL queries write to Kafka?
Answer: C,D
Explanation:
SHOW STREAMS and EXPLAIN <query> statements run against the KSQL server that the KSQL client is connected to. They don't communicate directly with Kafka. CREATE STREAM WITH <topic> and CREATE TABLE WITH <topic> write metadata to the KSQL command topic. Persistent queries based on CREATE STREAM AS SELECT and CREATE TABLE AS SELECT read and write to Kafka topics.
Non-persistent queries based on SELECT that are stateless only read from Kafka topics, for example:
SELECT A FROM foo WHERE A;
Non-persistent queries that are stateful read and write to Kafka, for example, COUNT and JOIN. The data in Kafka is deleted automatically when you terminate the query with CTRL-C.
NEW QUESTION # 67
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: D
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 # 68
How often is log compaction evaluated?
Answer: B
Explanation:
Log compaction is evaluated every time a segment is closed. It will be triggered if enough data is "dirty" (see dirty ratio config)
NEW QUESTION # 69
You need to explain the best reason to implement the consumer callback interface ConsumerRebalanceListener prior to a Consumer Group Rebalance.
Which statement is correct?
Answer: D
Explanation:
The ConsumerRebalanceListener lets youhandle partition assignments and revocationsduring rebalances.
This is critical for managingoffsets,stateful processing, orexternal transactions.
FromKafka Consumer Rebalance Docs:
"Implementing ConsumerRebalanceListener allows your application to take action before and after partitions are reassigned."
* A is true: It lets your app react when partitionsassigned to the consumer change.
* B, C, and D are unrelated to consumer rebalancing directly.
Reference:Kafka Consumer JavaDocs > ConsumerRebalanceListener
NEW QUESTION # 70
(You are implementing a Kafka Streams application to process financial transactions.
Each transaction must be processed exactly once to ensure accuracy.
The application reads from an input topic, performs computations, and writes results to an output topic.
During testing, you notice duplicate entries in the output topic, which violates the exactly-once processing requirement.
You need to ensure exactly-once semantics (EOS) for this Kafka Streams application.
Which step should you take?)
Answer: D
Explanation:
According to the official Apache Kafka documentation for Kafka Streams, exactly-once semantics (EOS) are controlled using the processing.guarantee configuration. To ensure that each input record is processed once and only once, and that the results are written atomically to the output topic, Kafka Streams provides built-in EOS support.
The correct and recommended configuration is processing.guarantee=exactly_once_v2, which is the default EOS implementation starting from Kafka 3.0. This mode uses Kafka's transactional APIs to atomically commit consumer offsets and produce output records within a single transaction. As documented, this prevents duplicates even in the presence of failures, retries, or application restarts.
Option A is incorrect because log compaction does not guarantee exactly-once processing; it only retains the latest record per key. Option B is insufficient because enable.idempotence alone does not handle consumer offset commits or state store consistency in Kafka Streams. Option C is invalid because no such configuration exists in Kafka Streams.
Therefore, setting processing.guarantee=exactly_once_v2 is the only fully correct and officially supported solution for achieving EOS in Kafka Streams applications.
NEW QUESTION # 71
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