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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Apache Kafka Fundamentals | 23% | - Configuration basics and cluster setup - Delivery semantics: at-most-once, at-least-once, exactly-once - Core concepts: brokers, topics, partitions, replicas, offsets - Kafka architecture and data flow |
| Topic 2: Kafka Connect | 15% | - Connect architecture: workers, connectors, tasks, converters - Source and sink connectors configuration - Data transformation and integration patterns |
| Topic 3: Application Observability | 13% | - Logging and monitoring metrics - Error diagnosis and troubleshooting - Tracing and performance tuning |
| Topic 4: Application Testing | 8% | - Unit testing producers and consumers - Mocking and test utilities - Integration testing and test clusters |
| Topic 5: Apache Kafka Application Development | 28% | - Consumer API: configuration, offset management, consumer groups, rebalancing - Transactional messaging and idempotence - Schema management: Confluent Schema Registry, compatibility rules, evolution - Producer API: configuration, serialization, partitioning, error handling |
| Topic 6: Apache Kafka Streams | 12% | - Streams API concepts: topology, processors, state stores - Windowing and time semantics - Error handling and state management - Stream processing operations: map, filter, join, aggregation |
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NEW QUESTION # 74
(A consumer application needs to use an at-most-once delivery semantic.
What is the best consumer configuration and code skeleton to avoid duplicate messages being read?)
Answer: C
Explanation:
According to the official Apache Kafka consumer semantics documentation, at-most-once delivery means that records may be lost but must never be processed more than once. To achieve this guarantee, a consumer must commit offsets before processing records. If the application crashes after committing but before processing, messages are skipped rather than reprocessed, which satisfies at-most-once semantics.
Option B correctly implements this pattern. It disables automatic offset commits (enable.auto.commit=false) and explicitly calls commitAsync() immediately after polling and before processing records. This ensures that offsets are advanced regardless of whether processing succeeds, eliminating the possibility of duplicate processing.
Option C represents at-least-once semantics, because offsets are committed after processing, which can lead to duplicates on failure. Options A and D rely on automatic commits or incorrect ordering and do not provide strict at-most-once guarantees.
The Kafka documentation explicitly states that manual offset management with commits before processing is required for at-most-once delivery.
Therefore, Option B is the best and officially documented configuration and code skeleton to avoid duplicate message processing.
NEW QUESTION # 75
We would like to be in an at-most once consuming scenario. Which offset commit strategy would you recommend?
Answer: C
Explanation:
Here, we must commit the offsets right after receiving a batch from a call to .poll()
NEW QUESTION # 76
Which SMTs is used to change the data type?
Answer: A
NEW QUESTION # 77
(Which configuration determines the maximum number of records a consumer can poll in a single call to poll()?)
Answer: D
Explanation:
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.
NEW QUESTION # 78
Which of the following setting increases the chance of batching for a Kafka Producer?
Answer: D
Explanation:
linger.ms forces the producer to wait to send messages, hence increasing the chance of creating batches
NEW QUESTION # 79
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