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| Section | Weight | Objectives |
|---|---|---|
| Security | 5-10% | - Encryption in Transit - Securing Schema Registry - Authentication (SASL, SSL/TLS) - Authorization (ACLs) |
| Developing with Kafka Producers | 15-20% | - Idempotent Producers - Producer Interceptors - Partitioning Strategies - Exactly-Once Semantics - Serialization (JSON, Avro, Protobuf) - Producer Configuration and Tuning - Error Handling and Retry Logic |
| Monitoring and Operations | 5-10% | - Kafka Metrics and Monitoring - Consumer Lag and Health Checks - Cross-region Replication - Disaster Recovery - Log Compaction |
| Installing and Configuring Kafka | 5-10% | - ZooKeeper vs KRaft Mode - Broker Configuration Parameters - Topic Creation and Configuration - Partition Assignment Strategies |
| Developing with Kafka Consumers | 15-20% | - Deserialization - Offset Management - Consumer Groups and Rebalancing - Consumer Configuration and Tuning - Multi-threaded Consumers - Consumer Interceptors - Commit Strategies (Auto, Manual, Cooperative) |
| Schema Management | 5-10% | - Schema Registry Architecture - Schema Evolution and Compatibility - Protobuf and JSON Schemas - Schema Registration and Retrieval - Avro Schemas |
| Kafka Streams | 20-25% | - Interactive Queries - Testing Kafka Streams Applications - State Stores and Processors - Windowed Operations (Tumbling, Hopping, Session) - Exactly-Once Processing - Streams Architecture and Topology - Transformation Operations (map, filter, join, aggregate) |
| Kafka Fundamentals | 10-15% | - Replication Factor and ISR - Brokers and Clusters - Topics, Partitions, and Offsets - Producer and Consumer Basics - Log Segments and Retention |
| Kafka Connect | 10-15% | - Source and Sink Connectors - Converters and Transforms - Connect Architecture (Workers, Connectors, Tasks) - Connector Configuration - Single Message Transformations (SMT) - Offset Management in Connect |
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NEW QUESTION # 90
(A consumer application needs to use an at-most-once delivery semantic.
What is the best consumer configuration and code skeleton to avoid duplicate messages being read?)
Answer: D
Explanation:
According to the official Apache Kafka consumer semantics documentation, at-most-once delivery means that records may be lost but must never be processed more than once. To achieve this guarantee, a consumer must commit offsets before processing records. If the application crashes after committing but before processing, messages are skipped rather than reprocessed, which satisfies at-most-once semantics.
Option B correctly implements this pattern. It disables automatic offset commits (enable.auto.commit=false) and explicitly calls commitAsync() immediately after polling and before processing records. This ensures that offsets are advanced regardless of whether processing succeeds, eliminating the possibility of duplicate processing.
Option C represents at-least-once semantics, because offsets are committed after processing, which can lead to duplicates on failure. Options A and D rely on automatic commits or incorrect ordering and do not provide strict at-most-once guarantees.
The Kafka documentation explicitly states that manual offset management with commits before processing is required for at-most-once delivery.
Therefore, Option B is the best and officially documented configuration and code skeleton to avoid duplicate message processing.
NEW QUESTION # 91
You have a Kafka Connect cluster with multiple connectors.
One connector is not working as expected.
How can you find logs related to that specific connector?
Answer: A
Explanation:
To isolate logs for a specific connector, you can configure a separate logger and appender in the Connect worker's log4j.properties file, using the connector's name as the logging context.
From Kafka Connect Logging Docs:
"Kafka Connect loggers use hierarchical logger names. You can configure per-connector log levels and output files by extending log4j.properties." A and C change verbosity but don't separate logs.
D is false; targeted logging is possible.
Reference: Kafka Connect > Logging and Debugging
NEW QUESTION # 92
You need to correctly join data from two Kafka topics.
What will allow for the co-partitioning? (Choose 2.)
Answer: A,B
NEW QUESTION # 93
Which of the following Kafka Streams operators are stateless? (select all that apply)
Answer: B,C,D,E,F
Explanation:
Seehttps://kafka.apache.org/20/documentation/streams/developer-guide/dsl-api.html#stateless-transformations
NEW QUESTION # 94
You create a topic named loT-Data with 10 partitions and replication factor of three.
A producer sends 1 MB messages compressed with Gzip.
Which two statements are true in this scenario?
(Select two.)
Answer: A,C
Explanation:
* A:Kafka batches storecompression metadata in their headers, indicating how the batch was compressed.
* B:Compression is controlled via theproducer config compression.type, which defaults to none.
Producers explicitly set Gzip or other formats.
FromKafka Producer Docs:
"Compression is done by the producer, and the type is stored in the message batch metadata. The broker does not recompress."
* C is false: messages are serializedbeforecompression.
* D is false: messages without keys are distributed acrossmultiple partitions.
Reference:Kafka Producer Configs > compression.type
NEW QUESTION # 95
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