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| Section | Weight | Objectives |
|---|---|---|
| Kafka Streams | 20-25% | - State Stores and Processors - Streams Architecture and Topology - Transformation Operations (map, filter, join, aggregate) - Exactly-Once Processing - Windowed Operations (Tumbling, Hopping, Session) - Interactive Queries - Testing Kafka Streams Applications |
| Schema Management | 5-10% | - Avro Schemas - Schema Evolution and Compatibility - Protobuf and JSON Schemas - Schema Registration and Retrieval - Schema Registry Architecture |
| Security | 5-10% | - Authentication (SASL, SSL/TLS) - Securing Schema Registry - Authorization (ACLs) - Encryption in Transit |
| Kafka Connect | 10-15% | - Single Message Transformations (SMT) - Connect Architecture (Workers, Connectors, Tasks) - Offset Management in Connect - Connector Configuration - Source and Sink Connectors - Converters and Transforms |
| Kafka Fundamentals | 10-15% | - Replication Factor and ISR - Topics, Partitions, and Offsets - Log Segments and Retention - Producer and Consumer Basics - Brokers and Clusters |
| Installing and Configuring Kafka | 5-10% | - Topic Creation and Configuration - Partition Assignment Strategies - Broker Configuration Parameters - ZooKeeper vs KRaft Mode |
| Monitoring and Operations | 5-10% | - Log Compaction - Kafka Metrics and Monitoring - Disaster Recovery - Cross-region Replication - Consumer Lag and Health Checks |
| Developing with Kafka Consumers | 15-20% | - Consumer Configuration and Tuning - Consumer Interceptors - Commit Strategies (Auto, Manual, Cooperative) - Multi-threaded Consumers - Deserialization - Consumer Groups and Rebalancing - Offset Management |
| Developing with Kafka Producers | 15-20% | - Producer Configuration and Tuning - Idempotent Producers - Exactly-Once Semantics - Producer Interceptors - Error Handling and Retry Logic - Serialization (JSON, Avro, Protobuf) - Partitioning Strategies |
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NEW QUESTION # 45
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: C
Explanation:
If a message key is not provided, Kafka's default partitioner uses round-robin distribution across available partitions.
From Kafka 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 is not preserved across partitions without a key.
Reference: Kafka Producer Partitioner Behavior
NEW QUESTION # 46
A consumer starts and has auto.offset.reset=none, and the topic partition currently has data for offsets going from 45 to 2311. The consumer group has committed the offset 10 for the topic before. Where will the consumer read from?
Answer: C
Explanation:
auto.offset.reset=none means that the consumer will crash if the offsets it's recovering from have been deleted from Kafka, which is the case here, as 10 < 45
NEW QUESTION # 47
while (true) {
ConsumerRecords<String, String> records = consumer.poll(100);
try {
consumer.commitSync();
} catch (CommitFailedException e) {
log.error("commit failed", e)
}
for (ConsumerRecord<String, String> record records)
{
System.out.printf("topic = %s, partition = %s, offset =
%d, customer = %s, country = %s
",
record.topic(), record.partition(),
record.offset(), record.key(), record.value());
}
}
What kind of delivery guarantee this consumer offers?
Answer: A
Explanation:
Here offset is committed before processing the message. If consumer crashes before processing the message, message will be lost when it comes back up.
NEW QUESTION # 48
What is true about replicas ?
Answer: A
Explanation:
Replicas are passive - they don't handle produce or consume request. Produce and consume requests get sent to the node hosting partition leader.
NEW QUESTION # 49
Which message delivery semantic is guaranteed by Kafka Connect?
Answer: B
NEW QUESTION # 50
......
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