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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Monitoring and Operations | 5-10% | - Kafka Metrics and Monitoring - Disaster Recovery - Cross-region Replication - Log Compaction - Consumer Lag and Health Checks |
| Topic 2: Installing and Configuring Kafka | 5-10% | - Topic Creation and Configuration - Partition Assignment Strategies - Broker Configuration Parameters - ZooKeeper vs KRaft Mode |
| Topic 3: Developing with Kafka Producers | 15-20% | - Producer Configuration and Tuning - Exactly-Once Semantics - Producer Interceptors - Error Handling and Retry Logic - Partitioning Strategies - Serialization (JSON, Avro, Protobuf) - Idempotent Producers |
| Topic 4: Kafka Connect | 10-15% | - Connector Configuration - Converters and Transforms - Single Message Transformations (SMT) - Source and Sink Connectors - Connect Architecture (Workers, Connectors, Tasks) - Offset Management in Connect |
| Topic 5: Kafka Streams | 20-25% | - Interactive Queries - Exactly-Once Processing - Windowed Operations (Tumbling, Hopping, Session) - Testing Kafka Streams Applications - Streams Architecture and Topology - State Stores and Processors - Transformation Operations (map, filter, join, aggregate) |
| Topic 6: Schema Management | 5-10% | - Protobuf and JSON Schemas - Schema Evolution and Compatibility - Avro Schemas - Schema Registration and Retrieval - Schema Registry Architecture |
| Topic 7: Developing with Kafka Consumers | 15-20% | - Offset Management - Consumer Groups and Rebalancing - Commit Strategies (Auto, Manual, Cooperative) - Multi-threaded Consumers - Consumer Configuration and Tuning - Deserialization - Consumer Interceptors |
| Topic 8: Security | 5-10% | - Securing Schema Registry - Authentication (SASL, SSL/TLS) - Authorization (ACLs) - Encryption in Transit |
| Topic 9: Kafka Fundamentals | 10-15% | - Topics, Partitions, and Offsets - Replication Factor and ISR - Producer and Consumer Basics - Log Segments and Retention - Brokers and Clusters |
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NEW QUESTION # 18
When using plain JSON data with Connect, you see the following error messageorg.apache.kafka.connect.
errors.DataExceptionJsonDeserializer with schemas.enable requires "schema" and "payload" fields and may not contain additional fields. How will you fix the error?
Answer: A
Explanation:
You will need to set the schemas.enable parameters for the converter to false for plain text with no schema.
NEW QUESTION # 19
Your configuration parameters for a Source connector and Connect worker are:
* offset.flush.interval.ms=60000
* offset.flush.timeout.ms=500
* offset.storage.topic=connect-offsets
* offset.storage.replication.factor=-1Which four statements match the expected behavior?(Select four.)
Answer: A,B,C,D
Explanation:
Each of these settings plays a key role inKafka Connect offset management:
* offset.flush.interval.ms=60000 # Offset commits happen every 60 seconds.
* offset.flush.timeout.ms=500 # The flush operation times out if it takes more than 500 ms.
* offset.storage.topic=connect-offsets # This is the internal topic for storing source offsets.
* offset.storage.replication.factor=-1 # This uses thebroker's default replication factor(commonly set via default.replication.factor).
FromKafka Connect Docs:
"If offset.storage.replication.factor=-1, the value will be determined by the broker's default replication factor." Reference:Kafka Connect Worker Configuration
NEW QUESTION # 20
You need to correctly join data from two Kafka topics.
What will allow for the co-partitioning? (Choose 2.)
Answer: A,C
NEW QUESTION # 21
(What are two stateless operations in the Kafka Streams API?
Select two.)
Answer: C,D
Explanation:
In the Kafka Streams API, operations are classified as stateless or stateful based on whether they require maintaining local state stores. According to the official Kafka Streams documentation, stateless operations process each record independently, without storing or accessing prior records.
Filter is a stateless operation because it evaluates each record individually and decides whether to pass it downstream. It does not require any state or historical context.
GroupBy is also considered stateless because it merely repartitions the stream by assigning a new key and forwarding records to downstream processors. While it triggers the creation of an internal repartition topic, the GroupBy operation itself does not maintain a state store.
In contrast, Reduce is a stateful operation because it aggregates records over time and requires maintaining intermediate results in a state store. Similarly, Join operations are stateful because Kafka Streams must buffer and store records from one or both input streams or tables to perform the join within a defined time window.
Thus, the correct stateless operations are Filter and GroupBy, as documented in the Kafka Streams developer guide.
NEW QUESTION # 22
Two consumers share the same group.id (consumer group id). Each consumer will
Answer: C
Explanation:
Each consumer is assigned a different partition of the topic to consume.
NEW QUESTION # 23
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