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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Kafka Connect | 15% | - Source and sink connectors configuration - Data transformation and integration patterns - Connect architecture: workers, connectors, tasks, converters |
| Topic 2: Application Observability | 13% | - Logging and monitoring metrics - Tracing and performance tuning - Error diagnosis and troubleshooting |
| Topic 3: Apache Kafka Fundamentals | 23% | - Configuration basics and cluster setup - Kafka architecture and data flow - Core concepts: brokers, topics, partitions, replicas, offsets - Delivery semantics: at-most-once, at-least-once, exactly-once |
| Topic 4: Apache Kafka Application Development | 28% | - Transactional messaging and idempotence - Producer API: configuration, serialization, partitioning, error handling - Schema management: Confluent Schema Registry, compatibility rules, evolution - Consumer API: configuration, offset management, consumer groups, rebalancing |
| Topic 5: Apache Kafka Streams | 12% | - Error handling and state management - Streams API concepts: topology, processors, state stores - Stream processing operations: map, filter, join, aggregation - Windowing and time semantics |
| Topic 6: Application Testing | 8% | - Unit testing producers and consumers - Integration testing and test clusters - Mocking and test utilities |
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NEW QUESTION # 62
(You create an Orders topic with 10 partitions.
The topic receives data at high velocity.
Your Kafka Streams application initially runs on a server with four CPU threads.
You move the application to another server with 10 CPU threads to improve performance.
What does this example describe?)
Answer: B
Explanation:
This scenario describes vertical scaling, as defined in the Apache Kafka Streams documentation and general distributed systems terminology. Vertical scaling refers to adding more resources (CPU, memory, or disk) to a single machine to increase processing capacity.
In this example, the Kafka Streams application is moved from a server with four CPU threads to one with ten CPU threads, increasing the available compute resources without increasing the number of application instances. Kafka Streams can take advantage of additional CPU threads through its num.stream.threads configuration, allowing more partitions to be processed in parallel within the same application instance.
Horizontal scaling (also known as scaling out) would involve adding more application instances across multiple servers, not upgrading a single server. Option D is therefore incorrect, and Option C is not a recognized scaling term.
Thus, this example clearly illustrates vertical scaling, where performance is improved by increasing the capacity of a single node.
NEW QUESTION # 63
Your configuration parameters for a Source connector and Connect worker are:
* offset.flush.interval.ms=60000
* offset.flush.timeout.ms=500
* offset.storage.topic=connect-offsets
* offset.storage.replication.factor=-1Which four statements match the expected behavior?(Select four.)
Answer: A,B,C,D
Explanation:
Each of these settings plays a key role inKafka Connect offset management:
* offset.flush.interval.ms=60000 # Offset commits happen every 60 seconds.
* offset.flush.timeout.ms=500 # The flush operation times out if it takes more than 500 ms.
* offset.storage.topic=connect-offsets # This is the internal topic for storing source offsets.
* offset.storage.replication.factor=-1 # This uses thebroker's default replication factor(commonly set via default.replication.factor).
FromKafka Connect Docs:
"If offset.storage.replication.factor=-1, the value will be determined by the broker's default replication factor." Reference:Kafka Connect Worker Configuration
NEW QUESTION # 64
You are building a consumer application that processes events from a Kafka topic. What is the most important metric to monitor to ensure real-time processing?
Answer: A
Explanation:
This metric shows the current lag (number of messages behind the broker)
NEW QUESTION # 65
(You have a Kafka consumer in production actively reading from a critical topic from which multiple other applications are consuming.
You have a new requirement to update the offset of your consumer to start reading from the beginning of the topic.
Which action would you take?)
Answer: A
Explanation:
The Apache Kafka documentation states that consumer offsets are stored per consumer group and must be explicitly reset if an application needs to reprocess data from an earlier position. The officially supported and safest way to reset offsets for an existing consumer group is by using the kafka-consumer-groups CLI tool with the --reset-offsets option and the --to-earliest flag.
Setting auto.offset.reset=earliest (Option D) only applies when no committed offset exists for a partition.
Since this consumer is already running in production, offsets are already committed, making this option ineffective. Option C does not change offsets because Kafka identifies consumers by group ID, not application instance. Option B is extremely dangerous and incorrect, as setting retention.ms=0 deletes data for all consumers and impacts other applications.
Therefore, using the CLI to reset offsets for the specific consumer group ensures controlled, auditable, and targeted offset management without affecting other consumers or the topic itself. This approach is explicitly documented in Kafka's consumer group administration tools.
NEW QUESTION # 66
How can you gracefully make a Kafka consumer to stop immediately polling data from Kafka and gracefully shut down a consumer application?
Answer: A
Explanation:
See https://stackoverflow.com/a/37748336/3019499
NEW QUESTION # 67
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