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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Developing with Kafka Producers | 15-20% | - Exactly-Once Semantics - Partitioning Strategies - Producer Configuration and Tuning - Producer Interceptors - Idempotent Producers - Error Handling and Retry Logic - Serialization (JSON, Avro, Protobuf) |
| Topic 2: Kafka Fundamentals | 10-15% | - Replication Factor and ISR - Brokers and Clusters - Producer and Consumer Basics - Topics, Partitions, and Offsets - Log Segments and Retention |
| Topic 3: Installing and Configuring Kafka | 5-10% | - ZooKeeper vs KRaft Mode - Topic Creation and Configuration - Broker Configuration Parameters - Partition Assignment Strategies |
| Topic 4: Schema Management | 5-10% | - Schema Registry Architecture - Schema Evolution and Compatibility - Schema Registration and Retrieval - Avro Schemas - Protobuf and JSON Schemas |
| Topic 5: Monitoring and Operations | 5-10% | - Log Compaction - Disaster Recovery - Kafka Metrics and Monitoring - Consumer Lag and Health Checks - Cross-region Replication |
| Topic 6: Security | 5-10% | - Authentication (SASL, SSL/TLS) - Encryption in Transit - Authorization (ACLs) - Securing Schema Registry |
| Topic 7: Kafka Connect | 10-15% | - Connector Configuration - Converters and Transforms - Connect Architecture (Workers, Connectors, Tasks) - Source and Sink Connectors - Single Message Transformations (SMT) - Offset Management in Connect |
| Topic 8: Kafka Streams | 20-25% | - Interactive Queries - State Stores and Processors - Streams Architecture and Topology - Transformation Operations (map, filter, join, aggregate) - Exactly-Once Processing - Testing Kafka Streams Applications - Windowed Operations (Tumbling, Hopping, Session) |
| Topic 9: Developing with Kafka Consumers | 15-20% | - Consumer Groups and Rebalancing - Consumer Interceptors - Multi-threaded Consumers - Offset Management - Commit Strategies (Auto, Manual, Cooperative) - Consumer Configuration and Tuning - Deserialization |
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NEW QUESTION # 36
How often is log compaction evaluated?
Answer: A
Explanation:
Log compaction is evaluated every time a segment is closed. It will be triggered if enough data is "dirty" (see dirty ratio config)
NEW QUESTION # 37
Which statement describes the storage location for a sink connector's offsets?
Answer: D
Explanation:
Kafka Connect sink connectors use the standard Kafka consumer mechanism to track offsets, which means offsets are stored in the __consumer_offsets internal topic.
From Kafka Connect Documentation:
"Sink connectors are regular Kafka consumers and store their offsets in the __consumer_offsets topic like any other consumer." The other options refer to source connectors or worker configuration.
offsets.storage.topic is relevant for source connectors.
Reference: Kafka Connect Concepts > Sink Connectors and Offset Storage
NEW QUESTION # 38
What is the risk of increasing max.in.flight.requests.per.connection while also enabling retries in a producer?
Answer: C
Explanation:
Some messages may require multiple retries. If there are more than 1 requests in flight, it may result in messages received out of order. Note an exception to this rule is if you enable the producer settingenable.idempotence=true which takes care of the out of ordering case on its own. Seehttps://issues.apache.org/jira/browse/KAFKA-5494
NEW QUESTION # 39
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 # 40
You need to configure a sink connector to write records that fail into a dead letter queue topic.
Requirements:
* Topic name: DLQ-Topic
* Headers containing error context must be added to the messagesWhich three configuration parameters are necessary?(Select three.)
Answer: B,E,F
Explanation:
To send failed records to adead letter queue (DLQ), you must configure:
* errors.tolerance=all: Tells the connector tonot failon errors but handle them (e.g., send to DLQ).
* errors.deadletterqueue.topic.name=DLQ-Topic: Specifies the DLQ topic.
* errors.deadletterqueue.context.headers.enable=true: Includes error context in message headers.
FromKafka Connect Error Handling Docs:
"Kafka Connect supports directing problematic records to a separate topic (DLQ) using errors.* configs.
Headers can include failure metadata."
Options D, E, F are related tologging, not DLQ behavior.
Reference:Kafka Connect Configurations > Error Handling
NEW QUESTION # 41
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