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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Schema Management | 5-10% | - Protobuf and JSON Schemas - Schema Registry Architecture - Avro Schemas - Schema Evolution and Compatibility - Schema Registration and Retrieval |
| Topic 2: Installing and Configuring Kafka | 5-10% | - Topic Creation and Configuration - ZooKeeper vs KRaft Mode - Broker Configuration Parameters - Partition Assignment Strategies |
| Topic 3: Monitoring and Operations | 5-10% | - Log Compaction - Kafka Metrics and Monitoring - Disaster Recovery - Cross-region Replication - Consumer Lag and Health Checks |
| Topic 4: Security | 5-10% | - Authentication (SASL, SSL/TLS) - Securing Schema Registry - Authorization (ACLs) - Encryption in Transit |
| Topic 5: Kafka Streams | 20-25% | - Testing Kafka Streams Applications - Interactive Queries - Exactly-Once Processing - State Stores and Processors - Streams Architecture and Topology - Windowed Operations (Tumbling, Hopping, Session) - Transformation Operations (map, filter, join, aggregate) |
| Topic 6: Developing with Kafka Consumers | 15-20% | - Deserialization - Commit Strategies (Auto, Manual, Cooperative) - Consumer Interceptors - Multi-threaded Consumers - Offset Management - Consumer Configuration and Tuning - Consumer Groups and Rebalancing |
| Topic 7: Kafka Connect | 10-15% | - Connect Architecture (Workers, Connectors, Tasks) - Single Message Transformations (SMT) - Connector Configuration - Converters and Transforms - Source and Sink Connectors - Offset Management in Connect |
| Topic 8: Developing with Kafka Producers | 15-20% | - Producer Configuration and Tuning - Error Handling and Retry Logic - Exactly-Once Semantics - Partitioning Strategies - Serialization (JSON, Avro, Protobuf) - Idempotent Producers - Producer Interceptors |
| 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 # 67
A producer is sending messages with null key to a topic with 6 partitions using the DefaultPartitioner. Where will the messages be stored?
Answer: D
Explanation:
Message with no keys will be stored with round-robin strategy among partitions.
NEW QUESTION # 68
A topic receives all the orders for the products that are available on a commerce site. Two applications want to process all the messages independently - order fulfilment and monitoring. The topic has 4 partitions, how would you organise the consumers for optimal performance and resource usage?
Answer: C
Explanation:
two partitions groups - one for each application so that all messages are delivered to both the application. 4 consumers in each as there are 4 partitions of the topic, and you cannot have more consumers per groups than the number of partitions (otherwise they will be inactive and wasting resources)
NEW QUESTION # 69
How do Kafka brokers ensure great performance between the producers and consumers? (select two)
Answer: A,B
Explanation:
Kafka transfers data with zero-copy and sends the raw bytes it receives from the producer straight to the consumer, leveraging the RAM available as page cache
NEW QUESTION # 70
An application is consuming messages from Kafka.
The application logs show that partitions are frequently being reassigned within the consumer group.
Which two factors may be contributing to this?
(Select two.)
Answer: C,D
Explanation:
Frequentrebalancesin a consumer group occur when:
* Consumers are too slowand miss heartbeats (A), or
* Instances crash or restart, triggering group membership changes (D)
FromKafka Consumer Group Coordination Docs:
"If a consumer fails to send a heartbeat in time (due to slowness or crash), it is removed from the group, causing a rebalance." Option B (partition mismatch) affectsload balancing, not rebalance frequency.
Option C (broker storage) doesn't trigger consumer rebalances.
Reference:Kafka Consumer Group Coordination and Heartbeats
NEW QUESTION # 71
Clients that connect to a Kafka cluster are required to specify one or more brokers in the bootstrap.
servers parameter.
What is the primary advantage of specifying more than one broker?
Answer: D
Explanation:
The bootstrap.servers setting is used by clients (producers and consumers) toinitially connectto the Kafka cluster. It does not need to list all brokers-just a few that are up and reachable. The client will then use the cluster metadata to discover the full broker list.
FromKafka Java Client Configuration Docs:
"This is a list of host/port pairs to use for establishing the initial connection to the Kafka cluster. The client will make use of all servers irrespective of which servers are specified here for bootstrapping." Hence,listing multiple brokers provides redundancyand fault tolerance in the event one is down during client startup.
Reference:Apache Kafka Java Client Configuration > bootstrap.servers
NEW QUESTION # 72
......
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