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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Monitoring and Operations | 5-10% | - Cross-region Replication - Consumer Lag and Health Checks - Disaster Recovery - Kafka Metrics and Monitoring - Log Compaction |
| Topic 2: Developing with Kafka Consumers | 15-20% | - Commit Strategies (Auto, Manual, Cooperative) - Offset Management - Consumer Interceptors - Deserialization - Multi-threaded Consumers - Consumer Configuration and Tuning - Consumer Groups and Rebalancing |
| Topic 3: Installing and Configuring Kafka | 5-10% | - Topic Creation and Configuration - Broker Configuration Parameters - ZooKeeper vs KRaft Mode - Partition Assignment Strategies |
| Topic 4: Kafka Streams | 20-25% | - Windowed Operations (Tumbling, Hopping, Session) - Interactive Queries - Streams Architecture and Topology - State Stores and Processors - Testing Kafka Streams Applications - Transformation Operations (map, filter, join, aggregate) - Exactly-Once Processing |
| Topic 5: Schema Management | 5-10% | - Schema Registration and Retrieval - Protobuf and JSON Schemas - Schema Evolution and Compatibility - Avro Schemas - Schema Registry Architecture |
| Topic 6: Kafka Connect | 10-15% | - Connect Architecture (Workers, Connectors, Tasks) - Converters and Transforms - Offset Management in Connect - Single Message Transformations (SMT) - Connector Configuration - Source and Sink Connectors |
| Topic 7: Security | 5-10% | - Securing Schema Registry - Authorization (ACLs) - Encryption in Transit - Authentication (SASL, SSL/TLS) |
| Topic 8: Kafka Fundamentals | 10-15% | - Producer and Consumer Basics - Log Segments and Retention - Replication Factor and ISR - Brokers and Clusters - Topics, Partitions, and Offsets |
| Topic 9: Developing with Kafka Producers | 15-20% | - Error Handling and Retry Logic - Exactly-Once Semantics - Partitioning Strategies - Idempotent Producers - Serialization (JSON, Avro, Protobuf) - Producer Configuration and Tuning - Producer Interceptors |
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NEW QUESTION # 59
What data format isn't natively available with the Confluent REST Proxy?
Answer: B
Explanation:
Protocol buffers isn't a natively supported type for the Confluent REST Proxy, but you may use the binary format instead
NEW QUESTION # 60
You have a topic with four partitions. The application reads from it using two consumers in a single consumer group.
Processing is CPU-bound, and lag is increasing.
What should you do?
Answer: C
Explanation:
If the application is CPU-bound and lagging, adding more consumers to the group will allow better parallel processing, especially since the topic has 4 partitions, allowing up to 4 active consumers.
From Kafka Consumer Group Docs:
"Kafka achieves parallelism by distributing partitions across consumers in a group. Adding consumers helps reduce lag if partitions are underutilized." B may help but requires repartitioning and coordination.
C or D affects how much data is polled, not how fast it's processed.
Reference: Kafka Consumer Concepts > Parallelism and Scaling
NEW QUESTION # 61
(You are developing a Kafka Streams application with a complex topology that has multiple sources, processors, sinks, and sub-topologies.
You are working in a development environment and do not have access to a real Kafka cluster or topics.
You need to perform unit testing on your Kafka Streams application.
Which should you use?)
Answer: A
Explanation:
The official Apache Kafka Streams developer documentation clearly states that TopologyTestDriver is the recommended and supported tool for unit testing Kafka Streams applications without requiring a running Kafka cluster. It is specifically designed to test complex topologies consisting of multiple sources, processors, state stores, sinks, and even sub-topologies.
TopologyTestDriver allows developers to instantiate the Kafka Streams topology directly in memory and feed test records into input topics while reading results from output topics. This makes it ideal for fast, deterministic, and isolated unit tests. As documented, it supports testing stateful and stateless operations, windowed aggregations, joins, and exactly-once behavior at the topology level.
Option A is incorrect because TestProducer and TestConsumer are not official Kafka Streams testing utilities.
Option B is invalid because KafkaUnitTestDriver does not exist in Apache Kafka. Option D (MockProducer and MockConsumer) belongs to the Kafka Clients API and is suitable for producer/consumer testing, but it does not support Kafka Streams topologies or state stores.
Therefore, TopologyTestDriver is the only fully supported and officially documented solution for unit testing Kafka Streams applications without a real Kafka cluster.
NEW QUESTION # 62
Your application is consuming from a topic configured with a deserializer.
It needs to be resilient to badly formatted records ("poison pills"). You surround the poll() call with a try/catch for RecordDeserializationException.
You need to log the bad record, skip it, and continue processing.
Which action should you take in the catch block?
Answer: A
Explanation:
To skip a corrupted record and avoid failing the application, you must seek past the failed offset manually using consumer.seek(). This allows the application to resume consumption from the next offset.
From Kafka Consumer Error Handling Docs:
"On deserialization failure, you can catch RecordDeserializationException, log the error, and call seek() to the next offset to skip the bad record." A does not prevent re-processing the bad record.
C is invalid; there's no skip() method in the Kafka consumer API.
D results in service interruption - not ideal for resiliency.
Reference: Kafka Consumer API - Exception Handling and seek()
NEW QUESTION # 63
(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: D
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 # 64
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