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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Kafka Streams | 20-25% | - Streams Architecture and Topology - Testing Kafka Streams Applications - Transformation Operations (map, filter, join, aggregate) - Exactly-Once Processing - State Stores and Processors - Interactive Queries - Windowed Operations (Tumbling, Hopping, Session) |
| Topic 2: Schema Management | 5-10% | - Schema Registration and Retrieval - Schema Registry Architecture - Avro Schemas - Schema Evolution and Compatibility - Protobuf and JSON Schemas |
| Topic 3: Kafka Fundamentals | 10-15% | - Brokers and Clusters - Replication Factor and ISR - Topics, Partitions, and Offsets - Log Segments and Retention - Producer and Consumer Basics |
| Topic 4: Developing with Kafka Producers | 15-20% | - Exactly-Once Semantics - Idempotent Producers - Producer Configuration and Tuning - Serialization (JSON, Avro, Protobuf) - Error Handling and Retry Logic - Partitioning Strategies - Producer Interceptors |
| Topic 5: Developing with Kafka Consumers | 15-20% | - Consumer Groups and Rebalancing - Deserialization - Consumer Configuration and Tuning - Commit Strategies (Auto, Manual, Cooperative) - Consumer Interceptors - Multi-threaded Consumers - Offset Management |
| Topic 6: Monitoring and Operations | 5-10% | - Disaster Recovery - Kafka Metrics and Monitoring - Log Compaction - Cross-region Replication - Consumer Lag and Health Checks |
| Topic 7: Security | 5-10% | - Securing Schema Registry - Authentication (SASL, SSL/TLS) - Authorization (ACLs) - Encryption in Transit |
| Topic 8: Installing and Configuring Kafka | 5-10% | - Broker Configuration Parameters - Topic Creation and Configuration - ZooKeeper vs KRaft Mode - Partition Assignment Strategies |
| Topic 9: Kafka Connect | 10-15% | - Connect Architecture (Workers, Connectors, Tasks) - Converters and Transforms - Source and Sink Connectors - Connector Configuration - Single Message Transformations (SMT) - Offset Management in Connect |
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質問 # 16
(An S3 source connector named s3-connector stopped running.
You use the Kafka Connect REST API to query the connector and task status.
One of the three tasks has failed.
You need to restart the connector and all currently running tasks.
Which REST request will restart the connector instance and all its tasks?)
正解：A
解説：
According to the official Apache Kafka Connect REST API documentation, restarting a connector can be done at different granularities: connector-level, task-level, or both. By default, calling POST /connectors/
{name}/restart only restarts the connector instance, not its tasks.
To restart the connector and all of its tasks, the request must explicitly include the query parameter includeTasks=true. This instructs Kafka Connect to restart the connector and all associated tasks, regardless of whether they are currently running or failed.
Option B (onlyFailed=true) limits the restart to only failed tasks, which does not meet the requirement to restart all tasks. Option C restarts only the connector instance, leaving tasks untouched. Option D restarts a single specific task and does not restart the connector itself.
Therefore, the correct and officially documented REST request to restart the connector instance and all tasks is:
POST /connectors/s3-connector/restart?includeTasks=true
This ensures a full restart of the connector and its execution context across the Kafka Connect cluster.
質問 # 17
What client protocol is supported for the schema registry? (select two)
正解：A、D
解説：
clients can interact with the schema registry using the HTTP or HTTPS interface
質問 # 18
A stream processing application is consuming from a topic with five partitions. You run three instances of the application. Each instance has num.stream.threads=5.
You need to identify the number of stream tasks that will be created and how many will actively consume messages from the input topic.
正解：D
解説：
In Kafka Streams,the number of stream tasks = number of input partitions × num.stream.threads × number of instances,but only as many as the number of partitions can actively consume at once.
However, in this case,Kafka Streams assigns one task per partition, and because there are 5 partitions and
15 threads (3 instances × 5 threads),15 tasks are created, andall 15 can be activedepending on processing topology.
FromKafka Streams Developer Guide:
"Kafka Streams creates one task per input partition. If you increase the number of stream threads, it runs multiple tasks in parallel within a single instance." So,15 stream tasks will be created and 15 will be actively consuming.
Reference:Apache Kafka Streams Documentation > Concepts > Tasks and Threads
質問 # 19
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：B
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 # 20
What happens if you write the following code in your producer? producer.send(producerRecord).get()
正解：C
解説：
Using Future.get() to wait for a reply from Kafka will limit throughput.
質問 # 21
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