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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Kafka Fundamentals | 10-15% | - Log Segments and Retention - Producer and Consumer Basics - Topics, Partitions, and Offsets - Brokers and Clusters - Replication Factor and ISR |
| Topic 2: Developing with Kafka Producers | 15-20% | - Exactly-Once Semantics - Serialization (JSON, Avro, Protobuf) - Partitioning Strategies - Error Handling and Retry Logic - Producer Configuration and Tuning - Producer Interceptors - Idempotent Producers |
| Topic 3: Kafka Connect | 10-15% | - Offset Management in Connect - Converters and Transforms - Source and Sink Connectors - Connector Configuration - Single Message Transformations (SMT) - Connect Architecture (Workers, Connectors, Tasks) |
| Topic 4: Schema Management | 5-10% | - Schema Registry Architecture - Avro Schemas - Schema Evolution and Compatibility - Protobuf and JSON Schemas - Schema Registration and Retrieval |
| Topic 5: Monitoring and Operations | 5-10% | - Kafka Metrics and Monitoring - Consumer Lag and Health Checks - Disaster Recovery - Log Compaction - Cross-region Replication |
| Topic 6: Kafka Streams | 20-25% | - Streams Architecture and Topology - Windowed Operations (Tumbling, Hopping, Session) - State Stores and Processors - Exactly-Once Processing - Testing Kafka Streams Applications - Interactive Queries - Transformation Operations (map, filter, join, aggregate) |
| Topic 7: Developing with Kafka Consumers | 15-20% | - Multi-threaded Consumers - Commit Strategies (Auto, Manual, Cooperative) - Consumer Groups and Rebalancing - Consumer Configuration and Tuning - Offset Management - Deserialization - Consumer Interceptors |
| Topic 8: Security | 5-10% | - Securing Schema Registry - Authentication (SASL, SSL/TLS) - Authorization (ACLs) - Encryption in Transit |
| Topic 9: Installing and Configuring Kafka | 5-10% | - ZooKeeper vs KRaft Mode - Broker Configuration Parameters - Partition Assignment Strategies - Topic Creation and Configuration |
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質問 # 46
(Your application consumes from a topic configured with a deserializer.
You want the application to be resilient to badly formatted records (poison pills).
You surround the poll() call with a try/catch block for RecordDeserializationException.
You need to log the bad record, skip it, and continue processing other records.
Which action should you take in the catch block?)
正解：B
解説：
The Apache Kafka consumer documentation explains that when a RecordDeserializationException occurs, the consumer cannot continue polling until the problematic offset is skipped. Simply logging the error is insufficient, because the consumer will repeatedly fail on the same record.
The recommended pattern is to log the malformed record, extract its topic, partition, and offset from the exception, and then call consumer.seek() to move the consumer position to the next offset (offset + 1). This allows the application to skip the poison pill and resume processing subsequent records.
Option B is invalid because Kafka does not provide a consumer.skip() API. Option C is unnecessary if the application is designed to tolerate malformed records. Option D results in an infinite failure loop.
Therefore, seeking past the bad record after logging it is the correct and officially documented way to handle poison pill records while maintaining consumer liveness and resilience.
質問 # 47
Which statement describes the storage location for a sink connector's offsets?
正解：D
解説：
Kafka Connectsink connectorsuse thestandard Kafka consumer mechanismto track offsets, which means offsets are stored in the__consumer_offsets internal topic.
FromKafka Connect Documentation:
"Sink connectors are regular Kafka consumers and store their offsets in the __consumer_offsets topic like any other consumer."
* The other options refer tosource connectorsor worker configuration.
* offsets.storage.topic is relevant forsource connectors.
Reference:Kafka Connect Concepts > Sink Connectors and Offset Storage
質問 # 48
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?
正解：A
解説：
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
質問 # 49
(You are writing to a Kafka topic with producer configuration acks=all.
The producer receives acknowledgements from the broker but still creates duplicate messages due to network timeouts and retries.
You need to ensure that duplicate messages are not created.
Which producer configuration should you set?)
正解：C
解説：
The official Apache Kafka producer documentation states that setting enable.idempotence=true guarantees that messages are written to a partition exactly once, even in the presence of retries caused by network failures or broker timeouts. This feature prevents duplicate records by assigning producer sequence numbers and validating them on the broker side.
For idempotent producers to work correctly, retries must be enabled, which is why Kafka recommends a very large value such as retries=2147483647. Additionally, limiting max.in.flight.requests.per.connection to 1 ensures strict ordering during retries, preventing message reordering in older Kafka versions and providing the safest configuration.
Option A is irrelevant to producers. Option B explicitly disables idempotence, which causes duplicates.
Option D disables retries, which increases the risk of message loss.
Therefore, the correct and fully documented solution to eliminate duplicate messages is enabling idempotence with retries and a safe in-flight request limit, as shown in Option C.
質問 # 50
The producer code below features a Callback class with a method called onCompletion().
In the onCompletion() method, when the request is completed successfully, what does the value metadata.
offset() represent?
正解：B
解説：
The offset in the RecordMetadata object returned by the producer represents the position of the record in the partition - i.e., the sequential ID assigned by Kafka once the message is committed.
From Kafka Producer API Documentation:
"The offset is the position of the record in the partition. This is a unique, sequential number assigned by the broker." D refers to metadata.partition(), not offset().
B and C are unrelated to how Kafka handles committed offsets.
Reference: Kafka Producer Java API > RecordMetadata
質問 # 51
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