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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Security | 5-10% | - Authorization (ACLs) - Securing Schema Registry - Authentication (SASL, SSL/TLS) - Encryption in Transit |
| Topic 2: Installing and Configuring Kafka | 5-10% | - Broker Configuration Parameters - ZooKeeper vs KRaft Mode - Topic Creation and Configuration - Partition Assignment Strategies |
| Topic 3: Developing with Kafka Consumers | 15-20% | - Multi-threaded Consumers - Consumer Groups and Rebalancing - Offset Management - Consumer Interceptors - Consumer Configuration and Tuning - Deserialization - Commit Strategies (Auto, Manual, Cooperative) |
| Topic 4: Kafka Fundamentals | 10-15% | - Log Segments and Retention - Topics, Partitions, and Offsets - Brokers and Clusters - Producer and Consumer Basics - Replication Factor and ISR |
| Topic 5: Kafka Connect | 10-15% | - Single Message Transformations (SMT) - Offset Management in Connect - Connector Configuration - Source and Sink Connectors - Converters and Transforms - Connect Architecture (Workers, Connectors, Tasks) |
| Topic 6: Schema Management | 5-10% | - Schema Registry Architecture - Avro Schemas - Schema Registration and Retrieval - Protobuf and JSON Schemas - Schema Evolution and Compatibility |
| Topic 7: Kafka Streams | 20-25% | - Interactive Queries - Exactly-Once Processing - Streams Architecture and Topology - Windowed Operations (Tumbling, Hopping, Session) - State Stores and Processors - Transformation Operations (map, filter, join, aggregate) - Testing Kafka Streams Applications |
| Topic 8: Monitoring and Operations | 5-10% | - Log Compaction - Kafka Metrics and Monitoring - Cross-region Replication - Consumer Lag and Health Checks - Disaster Recovery |
| Topic 9: Developing with Kafka Producers | 15-20% | - Serialization (JSON, Avro, Protobuf) - Partitioning Strategies - Exactly-Once Semantics - Idempotent Producers - Producer Configuration and Tuning - Error Handling and Retry Logic - Producer Interceptors |
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NEW QUESTION # 88
You are experiencing low throughput from a Java producer.
Metrics show low I/O thread ratio and low I/O thread wait ratio.
What is the most likely cause of the slow producer performance?
Answer: D
Explanation:
Low I/O thread activity with blocked throughput often indicates thatproducer callbacks are consuming too much time, causing the sender thread to block while waiting for onCompletion() to finish.
FromKafka Producer Performance Guide:
"Expensive logic in callbacks (e.g., I/O or complex computation) can block the sender thread, reducing throughput."
* Compression (A) may slightly impact CPU but not I/O thread usage.
* Large batches (B) improve throughput if managed correctly.
* A Layer 2 network issue (C) would lead to packet loss, not specifically low callback metrics.
Reference:Kafka Producer Metrics and Performance Tuning
NEW QUESTION # 89
A stream processing application is consuming from a topic with five partitions. You run three instances of the application. Each instance has num.stream.threads=5.
You need to identify the number of stream tasks that will be created and how many will actively consume messages from the input topic.
Answer: B
Explanation:
In Kafka Streams, the number of stream tasks = number of input partitions × num.stream.threads × number of instances, but only as many as the number of partitions can actively consume at once.
However, in this case, Kafka Streams assigns one task per partition, and because there are 5 partitions and 15 threads (3 instances × 5 threads), 15 tasks are created, and all 15 can be active depending on processing topology.
From Kafka Streams Developer Guide:
"Kafka Streams creates one task per input partition. If you increase the number of stream threads, it runs multiple tasks in parallel within a single instance." So, 15 stream tasks will be created and 15 will be actively consuming.
Reference: Apache Kafka Streams Documentation > Concepts > Tasks and Threads
NEW QUESTION # 90
What is encoding and decoding of a message also known as?
Answer: B
NEW QUESTION # 91
To continuously export data from Kafka into a target database, I should use
Answer: D
Explanation:
Kafka Connect Sink is used to export data from Kafka to external databases and Kafka Connect Source is used to import from external databases into Kafka.
NEW QUESTION # 92
Which of these joins does not require input topics to be sharing the same number of partitions?
Answer: B
Explanation:
GlobalKTables have their datasets replicated on each Kafka Streams instance and therefore no repartitioning is required
NEW QUESTION # 93
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