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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Developing with Kafka Consumers | 15-20% | - Consumer Groups and Rebalancing - Offset Management - Multi-threaded Consumers - Commit Strategies (Auto, Manual, Cooperative) - Consumer Interceptors - Consumer Configuration and Tuning - Deserialization |
| Topic 2: Kafka Connect | 10-15% | - Connect Architecture (Workers, Connectors, Tasks) - Converters and Transforms - Single Message Transformations (SMT) - Source and Sink Connectors - Connector Configuration - Offset Management in Connect |
| Topic 3: Kafka Fundamentals | 10-15% | - Producer and Consumer Basics - Topics, Partitions, and Offsets - Brokers and Clusters - Replication Factor and ISR - Log Segments and Retention |
| Topic 4: Installing and Configuring Kafka | 5-10% | - ZooKeeper vs KRaft Mode - Partition Assignment Strategies - Broker Configuration Parameters - Topic Creation and Configuration |
| Topic 5: Kafka Streams | 20-25% | - Exactly-Once Processing - Interactive Queries - Testing Kafka Streams Applications - Transformation Operations (map, filter, join, aggregate) - Streams Architecture and Topology - Windowed Operations (Tumbling, Hopping, Session) - State Stores and Processors |
| Topic 6: Security | 5-10% | - Encryption in Transit - Securing Schema Registry - Authentication (SASL, SSL/TLS) - Authorization (ACLs) |
| Topic 7: Developing with Kafka Producers | 15-20% | - Serialization (JSON, Avro, Protobuf) - Exactly-Once Semantics - Error Handling and Retry Logic - Producer Configuration and Tuning - Producer Interceptors - Idempotent Producers - Partitioning Strategies |
| Topic 8: Schema Management | 5-10% | - Schema Evolution and Compatibility - Schema Registration and Retrieval - Schema Registry Architecture - Avro Schemas - Protobuf and JSON Schemas |
| Topic 9: Monitoring and Operations | 5-10% | - Log Compaction - Kafka Metrics and Monitoring - Cross-region Replication - Disaster Recovery - Consumer Lag and Health Checks |
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NEW QUESTION # 37
How will you find out all the partitions where one or more of the replicas for the partition are not in-sync with the leader?
Answer: A
NEW QUESTION # 38
You want to sink data from a Kafka topic to S3 using Kafka Connect. There are 10 brokers in the cluster, the topic has 2 partitions with replication factor of 3. How many tasks will you configure for the S3 connector?
Answer: B
Explanation:
You cannot have more sink tasks (= consumers) than the number of partitions, so 2.
NEW QUESTION # 39
(You have a Kafka consumer in production actively reading from a critical topic from which multiple other applications are consuming.
You have a new requirement to update the offset of your consumer to start reading from the beginning of the topic.
Which action would you take?)
Answer: A
Explanation:
The Apache Kafka documentation states that consumer offsets are stored per consumer group and must be explicitly reset if an application needs to reprocess data from an earlier position. The officially supported and safest way to reset offsets for an existing consumer group is by using the kafka-consumer-groups CLI tool with the --reset-offsets option and the --to-earliest flag.
Setting auto.offset.reset=earliest (Option D) only applies when no committed offset exists for a partition.
Since this consumer is already running in production, offsets are already committed, making this option ineffective. Option C does not change offsets because Kafka identifies consumers by group ID, not application instance. Option B is extremely dangerous and incorrect, as setting retention.ms=0 deletes data for all consumers and impacts other applications.
Therefore, using the CLI to reset offsets for the specific consumer group ensures controlled, auditable, and targeted offset management without affecting other consumers or the topic itself. This approach is explicitly documented in Kafka's consumer group administration tools.
NEW QUESTION # 40
You want to send a message of size 3 MB to a topic with default message size configuration. How does KafkaProducer handle large messages?
Answer: A
Explanation:
MessageSizeTooLarge is not a retryable exception.
NEW QUESTION # 41
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: B,D
Explanation:
A: Kafka batches store compression metadata in their headers, indicating how the batch was compressed.
B: Compression is controlled via the producer config compression.type, which defaults to none. Producers explicitly set Gzip or other formats.
From Kafka 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 serialized before compression.
D is false: messages without keys are distributed across multiple partitions.
Reference: Kafka Producer Configs > compression.type
NEW QUESTION # 42
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