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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Kafka Connect | 10-15% | - Converters and Transforms - Connector Configuration - Single Message Transformations (SMT) - Source and Sink Connectors - Offset Management in Connect - Connect Architecture (Workers, Connectors, Tasks) |
| Topic 2: Installing and Configuring Kafka | 5-10% | - Topic Creation and Configuration - Partition Assignment Strategies - ZooKeeper vs KRaft Mode - Broker Configuration Parameters |
| Topic 3: Schema Management | 5-10% | - Schema Evolution and Compatibility - Schema Registration and Retrieval - Protobuf and JSON Schemas - Schema Registry Architecture - Avro Schemas |
| Topic 4: Security | 5-10% | - Encryption in Transit - Securing Schema Registry - Authentication (SASL, SSL/TLS) - Authorization (ACLs) |
| Topic 5: Monitoring and Operations | 5-10% | - Log Compaction - Disaster Recovery - Consumer Lag and Health Checks - Cross-region Replication - Kafka Metrics and Monitoring |
| Topic 6: Developing with Kafka Producers | 15-20% | - Idempotent Producers - Partitioning Strategies - Producer Interceptors - Exactly-Once Semantics - Serialization (JSON, Avro, Protobuf) - Producer Configuration and Tuning - Error Handling and Retry Logic |
| Topic 7: Kafka Fundamentals | 10-15% | - Brokers and Clusters - Log Segments and Retention - Topics, Partitions, and Offsets - Producer and Consumer Basics - Replication Factor and ISR |
| Topic 8: Kafka Streams | 20-25% | - Transformation Operations (map, filter, join, aggregate) - Windowed Operations (Tumbling, Hopping, Session) - Testing Kafka Streams Applications - Exactly-Once Processing - Streams Architecture and Topology - Interactive Queries - State Stores and Processors |
| Topic 9: Developing with Kafka Consumers | 15-20% | - Consumer Configuration and Tuning - Commit Strategies (Auto, Manual, Cooperative) - Consumer Groups and Rebalancing - Multi-threaded Consumers - Deserialization - Offset Management - Consumer Interceptors |
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NEW QUESTION # 52
Which statement is true about how exactly-once semantics (EOS) work in Kafka Streams?
Answer: A
Explanation:
Kafka Streams uses transactional producers to guarantee exactly-once semantics (EOS). This ensures that both the output records and state store updates are committed atomically, avoiding duplication or partial writes.
From Kafka Streams Documentation > Processing Guarantees:
"Kafka Streams leverages Kafka's transactional APIs to commit the output records and internal state updates as a single atomic unit, thereby providing exactly-once semantics." Option A is incorrect because log compaction is not disabled for EOS.
Option C incorrectly describes a checkpointing system Kafka Streams does not use.
Option D refers to deduplication, which is not how EOS is achieved in Streams.
Reference: Kafka Streams Processing Guarantees
NEW QUESTION # 53
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: B
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 # 54
A producer application in a developer machine was able to send messages to a Kafka topic. After copying the producer application into another developer's machine, the producer is able to connect to Kafka but unable to produce to the same Kafka topic because of an authorization issue. What is the likely issue?
Answer: C
Explanation:
ACLs take "Host" as a parameter, which represents an IP. It can be * (all IP), or a specific IP. Here, it's a specific IP as moving a producer to a different machine breaks the consumer, so the ACL needs to be updated
NEW QUESTION # 55
Which two statements are correct about transactions in Kafka?
(Select two.)
Answer: C,D
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
# C. Consumers can consume both committed and uncommitted transactions.By default, Kafka consumers only read committed messages if they are configured with isolation.level=read_committed. However, if configured as read_uncommitted, they can also consume uncommitted (potentially aborted) transactional messages.
From Kafka Documentation:
"The isolation.level setting controls whether the consumer will read only committed messages or all messages, including uncommitted messages from ongoing or aborted transactions."
# D. Information about producers and their transactions is stored in the _transaction_state topic.Kafka uses an internal topic named __transaction_state to maintain metadata about producer transactions. This topic is essential for tracking the transaction lifecycle, fencing, and recovery.
From Kafka Internals:
"Kafka stores the state of active and completed transactions in an internal topic called __transaction_state."
NEW QUESTION # 56
Using the Confluent Schema Registry, where are Avro schema stored?
Answer: D
Explanation:
The Schema Registry stores all the schemas in the _schemas Kafka topic
NEW QUESTION # 57
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