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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Application Observability | 13% | - Error diagnosis and troubleshooting - Logging and monitoring metrics - Tracing and performance tuning |
| Topic 2: Apache Kafka Application Development | 28% | - Producer API: configuration, serialization, partitioning, error handling - Schema management: Confluent Schema Registry, compatibility rules, evolution - Transactional messaging and idempotence - Consumer API: configuration, offset management, consumer groups, rebalancing |
| Topic 3: Apache Kafka Fundamentals | 23% | - Configuration basics and cluster setup - Core concepts: brokers, topics, partitions, replicas, offsets - Kafka architecture and data flow - Delivery semantics: at-most-once, at-least-once, exactly-once |
| Topic 4: Application Testing | 8% | - Integration testing and test clusters - Mocking and test utilities - Unit testing producers and consumers |
| Topic 5: Apache Kafka Streams | 12% | - Windowing and time semantics - Error handling and state management - Streams API concepts: topology, processors, state stores - Stream processing operations: map, filter, join, aggregation |
| Topic 6: Kafka Connect | 15% | - Connect architecture: workers, connectors, tasks, converters - Data transformation and integration patterns - Source and sink connectors configuration |
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質問 # 74
Clients that connect to a Kafka cluster are required to specify one or more brokers in the bootstrap.servers parameter.
What is the primary advantage of specifying more than one broker?
正解：B
解説：
The bootstrap.servers setting is used by clients (producers and consumers) to initially connect to the Kafka cluster. It does not need to list all brokers-just a few that are up and reachable. The client will then use the cluster metadata to discover the full broker list.
From Kafka Java Client Configuration Docs:
"This is a list of host/port pairs to use for establishing the initial connection to the Kafka cluster. The client will make use of all servers irrespective of which servers are specified here for bootstrapping." Hence, listing multiple brokers provides redundancy and fault tolerance in the event one is down during client startup.
Reference: Apache Kafka Java Client Configuration > bootstrap.servers
質問 # 75
(You deploy a Kafka Streams application with five application instances.
Kafka Streams stores application metadata using internal topics.
Auto-topic creation is disabled in the Kafka cluster.
Which statement about this scenario is true?)
正解：B
解説：
According to the official Apache Kafka Streams documentation, Kafka Streams relies on internal topics (such as changelog topics and repartition topics) to store state, task assignments, and application metadata. These topics are essential for fault tolerance, rebalancing, and state recovery.
When auto-topic creation is disabled, Kafka Streams cannot automatically create the required internal topics unless they already exist. In this situation, the Streams application will fail during startup with a fatal, non- retriable exception, indicating that the necessary internal topics could not be created.
Kafka Streams does not pause or wait for manual topic creation, nor does it operate without storing metadata.
Instead, it explicitly requires these topics to exist or be creatable. While administrators can manually pre- create the required internal topics with the correct configuration, failure to do so causes the application to terminate.
Therefore, the correct and documented behavior is that the Kafka Streams application terminates with a non- retriable exception when auto-topic creation is disabled and required internal topics are missing.
質問 # 76
How will you set the retention for the topic named 'Aumy-topic' to 1 hour?
正解：D
解説：
retention.ms can be configured at topic level while creating topic or by altering topic. It shouldn't be set at the broker level (log.retention.ms) as this would impact all the topics in the cluster, not just the one we are interested in
質問 # 77
What is accomplished by producing data to a topic with a message key?
正解：D
解説：
When amessage keyis specified in Kafka, the producer uses apartitioner(typically the default hash-based partitioner) todeterministically map all records with the same key to the same partition. Kafka guarantees order within a partition, so this enables per-key ordering.
FromKafka Producer Concepts:
"If a key is present, the producer will always route records with the same key to the same partition. Kafka preserves the order of records within a partition." This is essential for ordered processing and join semantics.
Reference:Kafka Producer Design > Partitions and Keys
質問 # 78
(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：B
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 # 79
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