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| Section | Weight | Objectives |
|---|---|---|
| Security | 5-10% | - Securing Schema Registry - Encryption in Transit - Authorization (ACLs) - Authentication (SASL, SSL/TLS) |
| Kafka Streams | 20-25% | - Testing Kafka Streams Applications - Transformation Operations (map, filter, join, aggregate) - Interactive Queries - Windowed Operations (Tumbling, Hopping, Session) - Exactly-Once Processing - Streams Architecture and Topology - State Stores and Processors |
| Kafka Connect | 10-15% | - Source and Sink Connectors - Connector Configuration - Connect Architecture (Workers, Connectors, Tasks) - Single Message Transformations (SMT) - Converters and Transforms - Offset Management in Connect |
| Monitoring and Operations | 5-10% | - Consumer Lag and Health Checks - Kafka Metrics and Monitoring - Cross-region Replication - Disaster Recovery - Log Compaction |
| Schema Management | 5-10% | - Schema Registry Architecture - Avro Schemas - Schema Evolution and Compatibility - Schema Registration and Retrieval - Protobuf and JSON Schemas |
| Kafka Fundamentals | 10-15% | - Producer and Consumer Basics - Brokers and Clusters - Topics, Partitions, and Offsets - Log Segments and Retention - Replication Factor and ISR |
| Developing with Kafka Producers | 15-20% | - Error Handling and Retry Logic - Exactly-Once Semantics - Partitioning Strategies - Producer Configuration and Tuning - Idempotent Producers - Serialization (JSON, Avro, Protobuf) - Producer Interceptors |
| Installing and Configuring Kafka | 5-10% | - Broker Configuration Parameters - ZooKeeper vs KRaft Mode - Topic Creation and Configuration - Partition Assignment Strategies |
| Developing with Kafka Consumers | 15-20% | - Deserialization - Multi-threaded Consumers - Commit Strategies (Auto, Manual, Cooperative) - Consumer Interceptors - Consumer Groups and Rebalancing - Offset Management - Consumer Configuration and Tuning |
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NEW QUESTION # 89
Which of these joins does not require input topics to be sharing the same number of partitions?
Answer: A
Explanation:
GlobalKTables have their datasets replicated on each Kafka Streams instance and therefore no repartitioning is required
NEW QUESTION # 90
What are two examples of performance metrics?
(Select two.)
Answer: A,F
Explanation:
* fetch-rate: Measures how frequently consumers fetch data - critical for monitoring throughput.
* incoming-byte-rate: Shows how much data is being written to the broker per second - a key producer-side metric.
FromKafka JMX Metrics Guide:
"fetch-rate, incoming-byte-rate, request-latency are key performance metrics for producers and consumers."
* B, C, E, and F areapplication-layer business metrics, not Kafka performance metrics.
Reference:Kafka Monitoring > JMX Metrics
NEW QUESTION # 91
What isn't a feature of the Confluent schema registry?
Answer: B
Explanation:
Data is stored on brokers.
NEW QUESTION # 92
In Kafka Streams, by what value are internal topics prefixed by?
Answer: D
Explanation:
In Kafka Streams, the application.id is also the underlying group.id for your consumers, and the prefix for all internal topics (repartition and state)
NEW QUESTION # 93
Which partition assignment minimizes partition movements between two assignments?
Answer: C
Explanation:
The StickyAssignor tries to minimize partition movement by preserving existing assignments as much as possible while still achieving a balanced assignment. This improves consumer stability and reduces rebalances.
From the Kafka Consumer Assignor Documentation:
"The StickyAssignor attempts to preserve as many existing assignments as possible, which helps minimize partition movement between rebalances." RoundRobinAssignor focuses on even distribution, not stability.
RangeAssignor groups partitions by topic and assigns them consecutively, but can lead to imbalances.
PartitionAssignor is an abstract base class, not an assignor used directly.
Reference: Kafka Consumer Assignor Docs
NEW QUESTION # 94
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