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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Kafka Connect | 15% | - Connect architecture: workers, connectors, tasks, converters - Data transformation and integration patterns - Source and sink connectors configuration |
| Topic 2: Apache Kafka Streams | 12% | - Streams API concepts: topology, processors, state stores - Error handling and state management - Stream processing operations: map, filter, join, aggregation - Windowing and time semantics |
| Topic 3: Application Observability | 13% | - Tracing and performance tuning - Logging and monitoring metrics - Error diagnosis and troubleshooting |
| Topic 4: Application Testing | 8% | - Mocking and test utilities - Unit testing producers and consumers - Integration testing and test clusters |
| Topic 5: Apache Kafka Fundamentals | 23% | - Core concepts: brokers, topics, partitions, replicas, offsets - Kafka architecture and data flow - Configuration basics and cluster setup - Delivery semantics: at-most-once, at-least-once, exactly-once |
| Topic 6: Apache Kafka Application Development | 28% | - Producer API: configuration, serialization, partitioning, error handling - Transactional messaging and idempotence - Schema management: Confluent Schema Registry, compatibility rules, evolution - Consumer API: configuration, offset management, consumer groups, rebalancing |
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NEW QUESTION # 10
You create a topic named stream-logs with:
* A replication factor of 3
* Four partitions
* Messages that are plain logs without a keyHow will messages be distributed across partitions?
Answer: D
Explanation:
If amessage key is not provided, Kafka's default partitioner usesround-robin distributionacross available partitions.
FromKafka Producer Design:
"If no key is provided, the default partitioner distributes messages round-robin to available partitions."
* A is incorrect - not all go to partition 0.
* C is invalid - Kafka doesn't group messages into one segment without a key.
* D is false - ordering isnot preservedacross partitions without a key.
Reference:Kafka Producer Partitioner Behavior
NEW QUESTION # 11
(You are implementing a Kafka Streams application to process financial transactions.
Each transaction must be processed exactly once to ensure accuracy.
The application reads from an input topic, performs computations, and writes results to an output topic.
During testing, you notice duplicate entries in the output topic, which violates the exactly-once processing requirement.
You need to ensure exactly-once semantics (EOS) for this Kafka Streams application.
Which step should you take?)
Answer: D
Explanation:
According to the official Apache Kafka documentation for Kafka Streams, exactly-once semantics (EOS) are controlled using the processing.guarantee configuration. To ensure that each input record is processed once and only once, and that the results are written atomically to the output topic, Kafka Streams provides built-in EOS support.
The correct and recommended configuration is processing.guarantee=exactly_once_v2, which is the default EOS implementation starting from Kafka 3.0. This mode uses Kafka's transactional APIs to atomically commit consumer offsets and produce output records within a single transaction. As documented, this prevents duplicates even in the presence of failures, retries, or application restarts.
Option A is incorrect because log compaction does not guarantee exactly-once processing; it only retains the latest record per key. Option B is insufficient because enable.idempotence alone does not handle consumer offset commits or state store consistency in Kafka Streams. Option C is invalid because no such configuration exists in Kafka Streams.
Therefore, setting processing.guarantee=exactly_once_v2 is the only fully correct and officially supported solution for achieving EOS in Kafka Streams applications.
NEW QUESTION # 12
You are composing a REST request to create a new connector in a running Connect cluster. You invoke POST /connectors with a configuration and receive a 409 (Conflict) response.
What are two reasons for this response? (Select two.)
Answer: B,D
Explanation:
A409 Conflictresponse in Kafka Connect REST API usually means:
* The connector name provided already exists (i.e., a connector with that name is already deployed).
* The Kafka Connect cluster is temporarily unavailable to accept new tasks due toa rebalance in progress.
From theConfluent Developer documentationand Kafka Connect REST API spec:
"409 - Conflict. The connector name is already in use or the worker group is in the process of rebalancing and cannot complete the request at this time." Option A is incorrect because invalid configurations return400 Bad Request, not 409.
Option B is invalid because Kafka Connect does not have a strict concept of "capacity" that prevents connector creation with a 409.
Reference:Apache Kafka Connect REST API documentation
NEW QUESTION # 13
You have a topic t1 with six partitions. You use Kafka Connect to send data from topic t1 in your Kafka cluster to Amazon S3. Kafka Connect is configured for two tasks.
How many partitions will each task process?
Answer: A
Explanation:
When using Kafka Connect with sink connectors (like S3 Sink),each task is assigned one or more topic partitions. Thetotal number of partitions (6)isevenly distributedacross the available tasks (2). Thus, each task will handle3 partitions.
FromKafka Connect Documentation:
"Kafka Connect divides the topic partitions among available tasks. For example, a topic with six partitions and two tasks results in each task handling three partitions." Reference:Kafka Connect Concepts > Tasks and Partitions
NEW QUESTION # 14
Which topic stores the sink connector offsets?
Answer: D
NEW QUESTION # 15
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