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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Security | 5-10% | - Encryption in Transit - Authentication (SASL, SSL/TLS) - Securing Schema Registry - Authorization (ACLs) |
| Topic 2: Schema Management | 5-10% | - Avro Schemas - Schema Evolution and Compatibility - Schema Registry Architecture - Schema Registration and Retrieval - Protobuf and JSON Schemas |
| Topic 3: Monitoring and Operations | 5-10% | - Cross-region Replication - Log Compaction - Kafka Metrics and Monitoring - Disaster Recovery - Consumer Lag and Health Checks |
| Topic 4: Kafka Fundamentals | 10-15% | - Brokers and Clusters - Log Segments and Retention - Replication Factor and ISR - Producer and Consumer Basics - Topics, Partitions, and Offsets |
| Topic 5: Kafka Connect | 10-15% | - Single Message Transformations (SMT) - Connect Architecture (Workers, Connectors, Tasks) - Offset Management in Connect - Connector Configuration - Source and Sink Connectors - Converters and Transforms |
| Topic 6: Developing with Kafka Producers | 15-20% | - Partitioning Strategies - Exactly-Once Semantics - Producer Interceptors - Error Handling and Retry Logic - Producer Configuration and Tuning - Idempotent Producers - Serialization (JSON, Avro, Protobuf) |
| Topic 7: Kafka Streams | 20-25% | - Streams Architecture and Topology - Transformation Operations (map, filter, join, aggregate) - State Stores and Processors - Interactive Queries - Testing Kafka Streams Applications - Windowed Operations (Tumbling, Hopping, Session) - Exactly-Once Processing |
| Topic 8: Installing and Configuring Kafka | 5-10% | - Topic Creation and Configuration - ZooKeeper vs KRaft Mode - Broker Configuration Parameters - Partition Assignment Strategies |
| Topic 9: Developing with Kafka Consumers | 15-20% | - Consumer Groups and Rebalancing - Consumer Configuration and Tuning - Multi-threaded Consumers - Deserialization - Commit Strategies (Auto, Manual, Cooperative) - Offset Management - Consumer Interceptors |
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NEW QUESTION # 68
Which two statements are correct when assigning partitions to the consumers in a consumer group using the assign() API?
(Select two.)
Answer: A,B
Explanation:
Using assign() givesmanual controlover partition assignment. In this mode:
* B is true: The applicationchooses partitionsdirectly without involving the group coordinator.
* C is true: Since no group coordination occurs,no rebalancinghappens if a consumer dies.
FromKafka Consumer API Docs:
"assign() manually assigns a list of partitions to this consumer. No coordination takes place and no rebalancing is triggered."
* A is false: subscribe() and assign() aremutually exclusive.
* D is incorrect: Partitions don't need to be equal across topics.
Reference:Kafka Consumer Java API > assign() and subscribe()
NEW QUESTION # 69
When running a ksqlDB for connectors, what does the IF NOT EXISTS clause do?
Answer: B
NEW QUESTION # 70
What is not a valid authentication mechanism in Kafka?
Answer: C
Explanation:
Learn more about security herehttps://kafka.apache.org/documentation/#security
NEW QUESTION # 71
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: D
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 # 72
Your Kafka cluster has five brokers. The topic t1 on the cluster has:
* Two partitions
* Replication factor = 4
* min.insync.replicas = 3You need strong durability guarantees for messages written to topic t1.You configure a producer acks=all and all the replicas for t1 are in-sync.How many brokers need to acknowledge a message before it is considered committed?
Answer: B
Explanation:
With acks=all, the leader waits formin.insync.replicasto acknowledge the message. Since min.insync.
replicas=3, Kafka will only commit the messageonce 3 brokers (leader + 2 followers)confirm they have the message.
FromKafka Documentation > Acks and Durability:
"If acks=all is specified, the producer will wait until the full set of in-sync replicas has acknowledged the record. The minimum number of in-sync replicas is controlled by min.insync.replicas." Even though the replication factor is 4, only3 acknowledgments are needed, as defined by min.insync.
replicas.
Reference:Apache Kafka Producer Configs > acks, min.insync.replicas
NEW QUESTION # 73
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