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| Section | Weight | Objectives |
|---|---|---|
| Developing with Kafka Consumers | 15-20% | - Multi-threaded Consumers - Consumer Configuration and Tuning - Offset Management - Deserialization - Consumer Groups and Rebalancing - Consumer Interceptors - Commit Strategies (Auto, Manual, Cooperative) |
| Kafka Streams | 20-25% | - State Stores and Processors - Transformation Operations (map, filter, join, aggregate) - Testing Kafka Streams Applications - Windowed Operations (Tumbling, Hopping, Session) - Exactly-Once Processing - Interactive Queries - Streams Architecture and Topology |
| Kafka Connect | 10-15% | - Connect Architecture (Workers, Connectors, Tasks) - Single Message Transformations (SMT) - Connector Configuration - Source and Sink Connectors - Converters and Transforms - Offset Management in Connect |
| Security | 5-10% | - Authentication (SASL, SSL/TLS) - Securing Schema Registry - Encryption in Transit - Authorization (ACLs) |
| Schema Management | 5-10% | - Schema Registration and Retrieval - Schema Registry Architecture - Avro Schemas - Schema Evolution and Compatibility - Protobuf and JSON Schemas |
| Developing with Kafka Producers | 15-20% | - Exactly-Once Semantics - Producer Interceptors - Serialization (JSON, Avro, Protobuf) - Partitioning Strategies - Error Handling and Retry Logic - Producer Configuration and Tuning - Idempotent Producers |
| Installing and Configuring Kafka | 5-10% | - Topic Creation and Configuration - ZooKeeper vs KRaft Mode - Broker Configuration Parameters - Partition Assignment Strategies |
| Monitoring and Operations | 5-10% | - Cross-region Replication - Log Compaction - Disaster Recovery - Kafka Metrics and Monitoring - Consumer Lag and Health Checks |
| Kafka Fundamentals | 10-15% | - Topics, Partitions, and Offsets - Producer and Consumer Basics - Brokers and Clusters - Replication Factor and ISR - Log Segments and Retention |
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質問 # 82
You have a consumer group with default configuration settings reading messages from your Kafka cluster.
You need to optimize throughput so the consumer group processes more messages in the same amount of time.
Which change should you make?
正解：C
解説：
To increase consumer throughput, one effective strategy is to increase the amount of data fetched in each poll by raising fetch.max.bytes or max.partition.fetch.bytes. This allows each poll to retrieve more records per request, improving processing efficiency.
From Kafka Consumer Config Docs:
"Increasing fetch size allows consumers to retrieve larger batches of messages, improving throughput and reducing request overhead." Removing consumers (A) may reduce parallelism.
Manual commit (C) adds complexity, not throughput.
Decreasing session timeout (D) risks unnecessary rebalances.
Reference: Kafka Consumer Configuration > fetch.max.bytes
質問 # 83
This schema excerpt is an example of which schema format?
package com.mycorp.mynamespace;
message SampleRecord {
int32 Stock = 1;
double Price = 2;
string Product_Name = 3;
}
正解：B
解説：
This syntax is a clear match to Protocol Buffers (Protobuf). It defines a schema with fields, types, and tags, which is a format supported by Kafka when using Protobuf-based schema registry serialization.
From Confluent Schema Registry Docs:
"Kafka supports Protobuf serialization, where schemas are written in .proto files and include fields with tags." int32, double, and string are standard Protobuf types.
Avro uses JSON-style schema.
JSON Schema uses JSON object structure, not .proto.
YAML is unrelated.
Reference: Confluent Schema Registry for Protobuf
質問 # 84
How do Kafka brokers ensure great performance between the producers and consumers? (select two)
正解：B、E
解説：
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
質問 # 85
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?
正解：B
解説：
If more than one similar config is specified, the smaller unit size will take precedence.
質問 # 86
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?
正解：A
解説：
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
質問 # 87
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