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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Kafka Connect | 15% | - Data transformation and integration patterns - Connect architecture: workers, connectors, tasks, converters - Source and sink connectors configuration |
| Topic 2: Application Observability | 13% | - Error diagnosis and troubleshooting - Tracing and performance tuning - Logging and monitoring metrics |
| Topic 3: Apache Kafka Fundamentals | 23% | - Core concepts: brokers, topics, partitions, replicas, offsets - Delivery semantics: at-most-once, at-least-once, exactly-once - Configuration basics and cluster setup - Kafka architecture and data flow |
| 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 - Streams API concepts: topology, processors, state stores - Stream processing operations: map, filter, join, aggregation - Error handling and state management |
| Topic 6: Apache Kafka Application Development | 28% | - Consumer API: configuration, offset management, consumer groups, rebalancing - Transactional messaging and idempotence - Producer API: configuration, serialization, partitioning, error handling - Schema management: Confluent Schema Registry, compatibility rules, evolution |
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NEW QUESTION # 25
A stream processing application is consuming from a topic that has five partitions. You run three instances of your application, each with num.streams.threads set to five.
You need to identify the amount of total stream tasks that will be created and the amount that will be actively consuming messages from the input topic.
Which option is correct?
Answer: C
NEW QUESTION # 26
By default, which replica will be elected as a partition leader? (select two)
Answer: A,D
Explanation:
Preferred leader is a broker that was leader when topic was created. It is preferred because when partitions are first created, the leaders are balanced between brokers. Otherwise, any of the in-sync replicas (ISR) will be elected leader, as long as unclean.leader.election=false (by default)
NEW QUESTION # 27
You have a Kafka cluster and all the topics have a replication factor of 3. One intern at your company stopped a broker, and accidentally deleted all the data of that broker on the disk. What will happen if the broker is restarted?
Answer: A
Explanation:
Kafka replication mechanism makes it resilient to the scenarios where the broker lose data on disk, but can recover from replicating from other brokers. This makes Kafka amazing!
NEW QUESTION # 28
You are writing to a topic with acks=all.
The producer receives acknowledgments but you notice duplicate messages.
You find that timeouts due to network delay are causing resends.
Which configuration should you use to prevent duplicates?
Answer: A
Explanation:
To ensureexactly-once deliveryand avoid duplicates even during retries:
* enable.idempotence=trueensures deduplication on the broker
* retries=2147483647allows unlimited retries on retriable errors
* max.in.flight.requests.per.connection=5is themaximum value that preserves message order with idempotence FromKafka Producer Config Docs:
"To achieve exactly-once semantics, set enable.idempotence=true, and max.in.flight.requests.per.connection #
5."
* A is unrelated (consumer-side)
* C disables retries
* D disables idempotence, leading to duplicates
Reference:Kafka Producer Configs > enable.idempotence, retries
NEW QUESTION # 29
Which configuration allows more time for the consumer poll to process records?
Answer: D
Explanation:
max.poll.interval.ms defines themaximum delay between invocations of poll()before the consumer is considered failed. It essentially gives consumers more time to process records before needing to poll again.
FromKafka Consumer Configuration Reference:
"max.poll.interval.ms: The maximum delay between invocations of poll() when using consumer group management. If poll() is not called before expiration, the consumer is considered failed."
* session.timeout.ms and heartbeat.interval.ms relate to group coordination and heartbeats.
* fetch.max.wait.ms affects how long the broker waits to accumulate data before sending a fetch response.
Reference:Apache Kafka Consumer Configs
NEW QUESTION # 30
......
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