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| Section | Weight | Objectives |
|---|---|---|
| Kafka Streams | 20-25% | - Transformation Operations (map, filter, join, aggregate) - Exactly-Once Processing - Interactive Queries - Windowed Operations (Tumbling, Hopping, Session) - Streams Architecture and Topology - State Stores and Processors - Testing Kafka Streams Applications |
| Security | 5-10% | - Encryption in Transit - Authentication (SASL, SSL/TLS) - Authorization (ACLs) - Securing Schema Registry |
| Kafka Connect | 10-15% | - Connector Configuration - Connect Architecture (Workers, Connectors, Tasks) - Single Message Transformations (SMT) - Source and Sink Connectors - Converters and Transforms - Offset Management in Connect |
| Installing and Configuring Kafka | 5-10% | - Partition Assignment Strategies - Topic Creation and Configuration - ZooKeeper vs KRaft Mode - Broker Configuration Parameters |
| Developing with Kafka Consumers | 15-20% | - Deserialization - Consumer Groups and Rebalancing - Consumer Configuration and Tuning - Offset Management - Consumer Interceptors - Multi-threaded Consumers - Commit Strategies (Auto, Manual, Cooperative) |
| Monitoring and Operations | 5-10% | - Log Compaction - Disaster Recovery - Kafka Metrics and Monitoring - Consumer Lag and Health Checks - Cross-region Replication |
| Schema Management | 5-10% | - Schema Evolution and Compatibility - Avro Schemas - Schema Registration and Retrieval - Protobuf and JSON Schemas - Schema Registry Architecture |
| Kafka Fundamentals | 10-15% | - Topics, Partitions, and Offsets - Replication Factor and ISR - Brokers and Clusters - Producer and Consumer Basics - Log Segments and Retention |
| Developing with Kafka Producers | 15-20% | - Producer Interceptors - Producer Configuration and Tuning - Idempotent Producers - Exactly-Once Semantics - Partitioning Strategies - Serialization (JSON, Avro, Protobuf) - Error Handling and Retry Logic |
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NEW QUESTION # 19
A stream processing application is tracking the user activity of online shopping carts. The application tracks items added to the cart, items removed from the cart, and items ordered from the cart throughout the day for each user.
You need to capture the data to identify possible periods of user inactivity.
Which type of Kafka Streams window should you use?
Answer: A
NEW QUESTION # 20
What is the risk of increasing max.in.flight.requests.per.connection while also enabling retries in a producer?
Answer: A
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 # 21
An application is writing AVRO messages using Schema Registry to topic t1. During this process, the Schema Registry becomes unavailable for a few seconds.
What is the expected impact to the application?
Answer: C
NEW QUESTION # 22
To prevent network-induced duplicates when producing to Kafka, I should use
Answer: A
Explanation:
Producer idempotence helps prevent the network introduced duplicates. More details herehttps://cwiki.apache.org/confluence/display/KAFKA/Idempotent+Producer
NEW QUESTION # 23
In Kafka Streams, by what value are internal topics prefixed by?
Answer: A
Explanation:
In Kafka Streams, the application.id is also the underlying group.id for your consumers, and the prefix for all internal topics (repartition and state)
NEW QUESTION # 24
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