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| Section | Weight | Objectives |
|---|---|---|
| Kafka Connect | 10-15% | - Offset Management in Connect - Source and Sink Connectors - Converters and Transforms - Single Message Transformations (SMT) - Connect Architecture (Workers, Connectors, Tasks) - Connector Configuration |
| Kafka Streams | 20-25% | - Transformation Operations (map, filter, join, aggregate) - State Stores and Processors - Exactly-Once Processing - Interactive Queries - Testing Kafka Streams Applications - Windowed Operations (Tumbling, Hopping, Session) - Streams Architecture and Topology |
| Monitoring and Operations | 5-10% | - Log Compaction - Kafka Metrics and Monitoring - Consumer Lag and Health Checks - Cross-region Replication - Disaster Recovery |
| Installing and Configuring Kafka | 5-10% | - Topic Creation and Configuration - Broker Configuration Parameters - ZooKeeper vs KRaft Mode - Partition Assignment Strategies |
| Security | 5-10% | - Encryption in Transit - Securing Schema Registry - Authorization (ACLs) - Authentication (SASL, SSL/TLS) |
| Developing with Kafka Producers | 15-20% | - Producer Configuration and Tuning - Producer Interceptors - Partitioning Strategies - Exactly-Once Semantics - Error Handling and Retry Logic - Idempotent Producers - Serialization (JSON, Avro, Protobuf) |
| Schema Management | 5-10% | - Avro Schemas - Schema Registry Architecture - Schema Registration and Retrieval - Schema Evolution and Compatibility - Protobuf and JSON Schemas |
| Kafka Fundamentals | 10-15% | - Log Segments and Retention - Producer and Consumer Basics - Topics, Partitions, and Offsets - Brokers and Clusters - Replication Factor and ISR |
| Developing with Kafka Consumers | 15-20% | - Consumer Configuration and Tuning - Commit Strategies (Auto, Manual, Cooperative) - Consumer Groups and Rebalancing - Offset Management - Consumer Interceptors - Deserialization - Multi-threaded Consumers |
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NEW QUESTION # 88
ksqIDB Lambda functions must be used inside what type of designated functions?
Answer: D
NEW QUESTION # 89
A consumer application needs to use an "at most once" delivery semantic.
What is the best consumer configuration lo avoid duplicate messages being read?
Answer: D
NEW QUESTION # 90
Which configuration determines how many bytes of data are collected before sending messages to the Kafka broker?
Answer: A
NEW QUESTION # 91
You are using JDBC source connector to copy data from 2 tables to two Kafka topics. There is one connector created with max.tasks equal to 2 deployed on a cluster of 3 workers. How many tasks are launched?
Answer: B
Explanation:
we have two tables, so the max number of tasks is 2
NEW QUESTION # 92
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: A,B
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 # 93
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