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| Section | Weight | Objectives |
|---|---|---|
| Developing with Kafka Producers | 15-20% | - Exactly-Once Semantics - Producer Interceptors - Producer Configuration and Tuning - Idempotent Producers - Partitioning Strategies - Error Handling and Retry Logic - Serialization (JSON, Avro, Protobuf) |
| Developing with Kafka Consumers | 15-20% | - Consumer Groups and Rebalancing - Consumer Configuration and Tuning - Offset Management - Deserialization - Commit Strategies (Auto, Manual, Cooperative) - Multi-threaded Consumers - Consumer Interceptors |
| Kafka Fundamentals | 10-15% | - Log Segments and Retention - Producer and Consumer Basics - Brokers and Clusters - Topics, Partitions, and Offsets - Replication Factor and ISR |
| Kafka Connect | 10-15% | - Connect Architecture (Workers, Connectors, Tasks) - Single Message Transformations (SMT) - Offset Management in Connect - Source and Sink Connectors - Connector Configuration - Converters and Transforms |
| Security | 5-10% | - Encryption in Transit - Securing Schema Registry - Authorization (ACLs) - Authentication (SASL, SSL/TLS) |
| Kafka Streams | 20-25% | - Windowed Operations (Tumbling, Hopping, Session) - Exactly-Once Processing - Streams Architecture and Topology - Interactive Queries - State Stores and Processors - Transformation Operations (map, filter, join, aggregate) - Testing Kafka Streams Applications |
| Installing and Configuring Kafka | 5-10% | - Partition Assignment Strategies - Broker Configuration Parameters - ZooKeeper vs KRaft Mode - Topic Creation and Configuration |
| Monitoring and Operations | 5-10% | - Cross-region Replication - Log Compaction - Disaster Recovery - Kafka Metrics and Monitoring - Consumer Lag and Health Checks |
| Schema Management | 5-10% | - Schema Registration and Retrieval - Schema Evolution and Compatibility - Protobuf and JSON Schemas - Schema Registry Architecture - Avro Schemas |
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NEW QUESTION # 69
(A stream processing application tracks user activity in online shopping carts, including items added, removed, and ordered throughout the day for each user.
You need to capture data to identify possible periods of user inactivity.
Which type of Kafka Streams window should you use?)
Answer: B
Explanation:
According to the official Apache Kafka Streams documentation, session windows are specifically designed to model periods of activity separated by gaps of inactivity. A session window groups events that occur close together in time and closes when no new events arrive within a defined inactivity gap.
In this use case, tracking user shopping cart behavior requires detecting when a user becomes inactive (for example, stops adding or removing items). Session windows naturally represent this pattern by creating a new window when activity resumes after a period of silence. This makes them ideal for modeling user sessions, cart abandonment detection, and inactivity analysis.
Hopping and tumbling windows (Options B and C) are fixed-time windows and are not well-suited for detecting inactivity, as they segment data based on clock time rather than behavior. Sliding windows are primarily used for continuous joins and do not explicitly model inactivity gaps.
Therefore, session windows are the correct and officially recommended choice for identifying periods of user inactivity in Kafka Streams applications.
NEW QUESTION # 70
Which Kafka CLI should you use to consume from a topic?
Answer: D
Explanation:
Examplekafka-console-consumer --bootstrap-server 127.0.0.1:9092 --topic test --from-beginning
NEW QUESTION # 71
You are using JDBC source connector to copy data from 2 tables to two Kafka topics. There is one connector created with max.tasks equal to 2 deployed on a cluster of 3 workers. How many tasks are launched?
Answer: A
Explanation:
we have two tables, so the max number of tasks is 2
NEW QUESTION # 72
You are working on a Kafka cluster with three nodes. You create a topic named orders with:
replication.factor = 3
min.insync.replicas = 2
acks = allWhat exception will be generated if two brokers are down due to network delay?
Answer: C
Explanation:
With acks=all and min.insync.replicas=2, Kafka requires at least two in-sync replicas to acknowledge a write.
If only one broker is alive, the condition fails, and NotEnoughReplicasException is thrown by the producer.
From Kafka Producer Exception Docs:
"NotEnoughReplicasException is thrown when the number of in-sync replicas is insufficient to satisfy acks=all with min.insync.replicas." NetworkException is generic and not raised here.
NotCoordinatorException is related to consumer group coordination.
NotLeaderForPartitionException is unrelated unless accessing an unassigned leader.
Reference: Kafka Producer Error Handling
NEW QUESTION # 73
Which configuration allows more time for the consumer poll to process records?
Answer: C
Explanation:
max.poll.interval.ms defines themaximum delay between invocations of poll()before the consumer is considered failed. It essentially gives consumers more time to process records before needing to poll again.
FromKafka Consumer Configuration Reference:
"max.poll.interval.ms: The maximum delay between invocations of poll() when using consumer group management. If poll() is not called before expiration, the consumer is considered failed."
* session.timeout.ms and heartbeat.interval.ms relate to group coordination and heartbeats.
* fetch.max.wait.ms affects how long the broker waits to accumulate data before sending a fetch response.
Reference:Apache Kafka Consumer Configs
NEW QUESTION # 74
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