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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Apache Kafka Streams | 12% | - Streams API concepts: topology, processors, state stores - Windowing and time semantics - Stream processing operations: map, filter, join, aggregation - Error handling and state management |
| Topic 2: Application Observability | 13% | - Error diagnosis and troubleshooting - Tracing and performance tuning - Logging and monitoring metrics |
| Topic 3: Kafka Connect | 15% | - Connect architecture: workers, connectors, tasks, converters - Source and sink connectors configuration - Data transformation and integration patterns |
| Topic 4: Apache Kafka Application Development | 28% | - Schema management: Confluent Schema Registry, compatibility rules, evolution - Transactional messaging and idempotence - Consumer API: configuration, offset management, consumer groups, rebalancing - Producer API: configuration, serialization, partitioning, error handling |
| Topic 5: Apache Kafka Fundamentals | 23% | - Delivery semantics: at-most-once, at-least-once, exactly-once - Core concepts: brokers, topics, partitions, replicas, offsets - Kafka architecture and data flow - Configuration basics and cluster setup |
| Topic 6: Application Testing | 8% | - Unit testing producers and consumers - Mocking and test utilities - Integration testing and test clusters |
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NEW QUESTION # 37
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: B
Explanation:
This metric shows the current lag (number of messages behind the broker)
NEW QUESTION # 38
When is the onCompletion() method called?
private class ProducerCallback implements Callback {
@Override
public void onCompletion(RecordMetadata recordMetadata, Exception e) {
if (e != null) {
e.printStackTrace();
}
}
}
ProducerRecord<String, String> record =
new ProducerRecord<>("topic1", "key1", "value1");
producer.send(record, new ProducerCallback());
Answer: D
Explanation:
Callback is invoked when a broker response is received.
NEW QUESTION # 39
Your producer is producing at a very high rate and the batches are completely full each time. How can you improve the producer throughput? (select two)
Answer: B,C
Explanation:
Increase linger.ms
Explanation:
batch.size controls how many bytes of data to collect before sending messages to the Kafka broker. Set this as high as possible, without exceeding available memory. Enabling compression can also help make more compact batches and increase the throughput of your producer. Linger.ms will have no effect as the batches are already full
NEW QUESTION # 40
What exceptions may be caught by the following producer? (select two)
ProducerRecord<String, String> record =
new ProducerRecord<>("topic1", "key1", "value1");
try {
producer.send(record);
} catch (Exception e) {
e.printStackTrace();
}
Answer: B,C
Explanation:
These are the client side exceptions that may be encountered before message is sent to the broker, and before a future is returned by the .send() method.
NEW QUESTION # 41
You are building a system for a retail store selling products to customers.
Which three datasets should you model as a GlobalKTable?
(Select three.)
Answer: A,C,D
Explanation:
AGlobalKTableis a replicated, read-only table available in full on all instances. It's best forreference or lookup datasetssuch as:
* Product catalog
* Customer profiles
* Warehouse inventory
FromKafka Streams Developer Guide:
"Use GlobalKTable when you need to perform joins using non-partition-aligned reference data that's small enough to replicate."
* Purchases and transactions are high-throughput, time-sensitive streams, not static reference data.
Reference:Kafka Streams Concepts > GlobalKTable
NEW QUESTION # 42
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