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| Section | Weight | Objectives |
|---|---|---|
| Kafka Connect | 10-15% | - Connector Configuration - Offset Management in Connect - Single Message Transformations (SMT) - Source and Sink Connectors - Connect Architecture (Workers, Connectors, Tasks) - Converters and Transforms |
| Developing with Kafka Consumers | 15-20% | - Consumer Groups and Rebalancing - Commit Strategies (Auto, Manual, Cooperative) - Consumer Configuration and Tuning - Deserialization - Offset Management - Consumer Interceptors - Multi-threaded Consumers |
| Security | 5-10% | - Authorization (ACLs) - Securing Schema Registry - Encryption in Transit - Authentication (SASL, SSL/TLS) |
| Installing and Configuring Kafka | 5-10% | - Partition Assignment Strategies - Broker Configuration Parameters - ZooKeeper vs KRaft Mode - Topic Creation and Configuration |
| Monitoring and Operations | 5-10% | - Kafka Metrics and Monitoring - Consumer Lag and Health Checks - Log Compaction - Cross-region Replication - Disaster Recovery |
| Kafka Fundamentals | 10-15% | - Producer and Consumer Basics - Brokers and Clusters - Topics, Partitions, and Offsets - Log Segments and Retention - Replication Factor and ISR |
| Kafka Streams | 20-25% | - Exactly-Once Processing - Transformation Operations (map, filter, join, aggregate) - Streams Architecture and Topology - Testing Kafka Streams Applications - Interactive Queries - State Stores and Processors - Windowed Operations (Tumbling, Hopping, Session) |
| Schema Management | 5-10% | - Schema Registration and Retrieval - Protobuf and JSON Schemas - Avro Schemas - Schema Registry Architecture - Schema Evolution and Compatibility |
| Developing with Kafka Producers | 15-20% | - Serialization (JSON, Avro, Protobuf) - Exactly-Once Semantics - Idempotent Producers - Partitioning Strategies - Producer Interceptors - Error Handling and Retry Logic - Producer Configuration and Tuning |
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質問 # 27
Where are KSQL-related data and metadata stored?
正解：B
解説：
metadata is stored in and built from the KSQL command topic. Each KSQL server has its own in-memory version of the metastore.
質問 # 28
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?
正解：D
解説：
To skip a corrupted record and avoid failing the application, you mustseek past the failed offsetmanually using consumer.seek(). This allows the application to resume consumption from the next offset.
FromKafka 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'sno skip() methodin the Kafka consumer API.
* D results in service interruption - not ideal for resiliency.
Reference:Kafka Consumer API - Exception Handling and seek()
質問 # 29
(You have a topic with four partitions. The application reading this topic is using a consumer group with two consumers.
Throughput is smoothly distributed among partitions, but application lag is increasing.
Application monitoring shows that message processing is consuming all available CPU resources.
Which action should you take to resolve this issue?)
正解：A
解説：
According to the Apache Kafka consumer and scalability documentation, parallelism in message processing is primarily determined by the number of partitions and the number of consumers within a consumer group.
Each partition can be assigned to only one consumer at a time. In this scenario, the topic already has four partitions, but only two consumers are processing them, meaning only two partitions are actively consumed in parallel.
Since monitoring indicates that message processing is CPU-bound, the bottleneck is not Kafka I/O but application-side processing. Adding more consumers (up to the number of partitions) allows Kafka to rebalance the partitions and distribute them across more consumer instances, increasing CPU parallelism and reducing lag.
Option A (adding more partitions) can increase parallelism but is more intrusive and may require repartitioning considerations for keys and downstream systems. Option B increases batch size, which can worsen CPU pressure. Option D may reduce per-poll workload but does not increase parallel processing capacity.
Therefore, the correct and recommended action per Kafka documentation is to add more consumers to the consumer group.
質問 # 30
(Which configuration determines the maximum number of records a consumer can poll in a single call to poll()?)
正解：D
解説：
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.
質問 # 31
You need to configure a sink connector to write records that fail into a dead letter queue topic. Requirements:
Topic name: DLQ-Topic
Headers containing error context must be added to the messagesWhich three configuration parameters are necessary?(Select three.)
正解：B、C、E
解説：
To send failed records to a dead letter queue (DLQ), you must configure:
errors.tolerance=all: Tells the connector to not fail on errors but handle them (e.g., send to DLQ).
errors.deadletterqueue.topic.name=DLQ-Topic: Specifies the DLQ topic.
errors.deadletterqueue.context.headers.enable=true: Includes error context in message headers.
From Kafka Connect Error Handling Docs:
"Kafka Connect supports directing problematic records to a separate topic (DLQ) using errors.* configs.
Headers can include failure metadata."
Options D, E, F are related to logging, not DLQ behavior.
Reference: Kafka Connect Configurations > Error Handling
質問 # 32
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