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| Section | Weight | Objectives |
|---|---|---|
| Developing with Kafka Consumers | 15-20% | - Deserialization - Commit Strategies (Auto, Manual, Cooperative) - Multi-threaded Consumers - Consumer Interceptors - Consumer Groups and Rebalancing - Offset Management - Consumer Configuration and Tuning |
| Kafka Streams | 20-25% | - Interactive Queries - Windowed Operations (Tumbling, Hopping, Session) - State Stores and Processors - Exactly-Once Processing - Transformation Operations (map, filter, join, aggregate) - Streams Architecture and Topology - Testing Kafka Streams Applications |
| Kafka Connect | 10-15% | - Single Message Transformations (SMT) - Connector Configuration - Connect Architecture (Workers, Connectors, Tasks) - Source and Sink Connectors - Converters and Transforms - Offset Management in Connect |
| Security | 5-10% | - Authentication (SASL, SSL/TLS) - Encryption in Transit - Securing Schema Registry - Authorization (ACLs) |
| Monitoring and Operations | 5-10% | - Consumer Lag and Health Checks - Cross-region Replication - Kafka Metrics and Monitoring - Disaster Recovery - Log Compaction |
| Kafka Fundamentals | 10-15% | - Log Segments and Retention - Brokers and Clusters - Topics, Partitions, and Offsets - Producer and Consumer Basics - Replication Factor and ISR |
| Schema Management | 5-10% | - Schema Registration and Retrieval - Protobuf and JSON Schemas - Avro Schemas - Schema Evolution and Compatibility - Schema Registry Architecture |
| Installing and Configuring Kafka | 5-10% | - Topic Creation and Configuration - Partition Assignment Strategies - Broker Configuration Parameters - ZooKeeper vs KRaft Mode |
| Developing with Kafka Producers | 15-20% | - Producer Configuration and Tuning - Error Handling and Retry Logic - Exactly-Once Semantics - Producer Interceptors - Partitioning Strategies - Idempotent Producers - Serialization (JSON, Avro, Protobuf) |
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NEW QUESTION # 35
Partition leader election is done by
Answer: C
Explanation:
The Controller is a broker that is responsible for electing partition leaders
NEW QUESTION # 36
You are sending messages to a Kafka cluster in JSON format and want to add more information related to each message:
Format of the message payload
Message creation time
A globally unique identifier that allows the message to be traced through the systemWhere should this additional information be set?
Answer: C
Explanation:
Kafka message headers are the right place to include metadata such as:
Payload format (e.g., schema version)
Timestamps (custom)
Trace or correlation IDs
From Kafka Producer API Docs:
"Headers are a map of key-value pairs that can be used to include metadata alongside the message, without modifying the message body." Keys determine partitioning and ordering Values are the message payload Brokers do not store arbitrary metadata fields Reference: Kafka ProducerRecord API > Headers
NEW QUESTION # 37
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: B
Explanation:
The bootstrap.servers setting is used by clients (producers and consumers) to initially connect to 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.
From Kafka 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 redundancy and fault tolerance in the event one is down during client startup.
Reference: Apache Kafka Java Client Configuration > bootstrap.servers
NEW QUESTION # 38
You are experiencing low throughput from a Java producer. You monitor the Kafka metrics and notice that the metrics for that producer show low I/O thread ratio and low I/O thread wait ratio.
What is most likely the cause of the slow producer performance?
Answer: C
NEW QUESTION # 39
Kafka is configured with following parameters - log.retention.hours = 168 log.retention.minutes = 168 log.retention.ms = 168 How long will the messages be retained for?
Answer: A
Explanation:
If more than one similar config is specified, the smaller unit size will take precedence.
NEW QUESTION # 40
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