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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Kafka Connect | 10-15% | - Connect Architecture (Workers, Connectors, Tasks) - Converters and Transforms - Single Message Transformations (SMT) - Source and Sink Connectors - Connector Configuration - Offset Management in Connect |
| Topic 2: Schema Management | 5-10% | - Schema Evolution and Compatibility - Avro Schemas - Schema Registry Architecture - Protobuf and JSON Schemas - Schema Registration and Retrieval |
| Topic 3: Installing and Configuring Kafka | 5-10% | - Broker Configuration Parameters - Topic Creation and Configuration - ZooKeeper vs KRaft Mode - Partition Assignment Strategies |
| Topic 4: Security | 5-10% | - Authentication (SASL, SSL/TLS) - Securing Schema Registry - Authorization (ACLs) - Encryption in Transit |
| Topic 5: Kafka Fundamentals | 10-15% | - Replication Factor and ISR - Topics, Partitions, and Offsets - Producer and Consumer Basics - Brokers and Clusters - Log Segments and Retention |
| Topic 6: Monitoring and Operations | 5-10% | - Log Compaction - Disaster Recovery - Kafka Metrics and Monitoring - Consumer Lag and Health Checks - Cross-region Replication |
| Topic 7: Developing with Kafka Producers | 15-20% | - Idempotent Producers - Exactly-Once Semantics - Producer Interceptors - Serialization (JSON, Avro, Protobuf) - Error Handling and Retry Logic - Partitioning Strategies - Producer Configuration and Tuning |
| Topic 8: Kafka Streams | 20-25% | - Testing Kafka Streams Applications - Exactly-Once Processing - Windowed Operations (Tumbling, Hopping, Session) - Streams Architecture and Topology - Interactive Queries - State Stores and Processors - Transformation Operations (map, filter, join, aggregate) |
| Topic 9: Developing with Kafka Consumers | 15-20% | - Multi-threaded Consumers - Deserialization - Offset Management - Consumer Groups and Rebalancing - Consumer Interceptors - Commit Strategies (Auto, Manual, Cooperative) - Consumer Configuration and Tuning |
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NEW QUESTION # 37
You are writing to a topic with acks=all.
The producer receives acknowledgments but you notice duplicate messages.
You find that timeouts due to network delay are causing resends.
Which configuration should you use to prevent duplicates?
Answer: C
Explanation:
To ensure exactly-once delivery and avoid duplicates even during retries:
enable.idempotence=true ensures deduplication on the broker
retries=2147483647 allows unlimited retries on retriable errors
max.in.flight.requests.per.connection=5 is the maximum value that preserves message order with idempotence From Kafka Producer Config Docs:
"To achieve exactly-once semantics, set enable.idempotence=true, and max.in.flight.requests.per.connection #
5."
A is unrelated (consumer-side)
C disables retries
D disables idempotence, leading to duplicates
Reference: Kafka Producer Configs > enable.idempotence, retries
NEW QUESTION # 38
The exactly once guarantee in the Kafka Streams is for which flow of data?
Answer: B
Explanation:
Kafka Streams can only guarantee exactly once processing if you have a Kafka to Kafka topology.
NEW QUESTION # 39
Match each configuration parameter with the correct deployment step in installing a Kafka connector.
Answer:
Explanation:
Explanation:
1st: Place the connector's JAR file in the directory specified by plugin.path
2nd: Restart the Kafka Connect cluster
3rd: Verify using REST API (/connector-plugins)
4th: Configure the connector
5th: (Repeat of 4th, duplicate step)
1st # Place the connector's JAR file in the directory specified by the plugin.path configuration.
2nd # Restart the Kafka Connect cluster.
3rd # Verify that the connector is installed by listing all available connectors using the Kafka Connect REST API (/connector-plugins).
4th # Configure the connector using a JSON or properties file with the necessary settings.
5th # Configure the connector using a JSON or properties file with the necessary settings.
Kafka Connect requires that custom connectors be placed in the directory defined by plugin.path. After restarting the cluster, you can use the REST API to confirm availability and then deploy the connector configuration.
From Kafka Connect Documentation:
"After placing the JAR in the plugin.path, you must restart the Connect cluster to pick it up. Use the
/connector-plugins REST endpoint to verify."
The duplication of configuration is an error in the question options and should occur only once.
Reference: Kafka Connect Plugin Installation Guide
NEW QUESTION # 40
(Your configuration parameters for a Source connector and Connect worker are:
* offset.flush.interval.ms=60000
* offset.flush.timeout.ms=500
* offset.storage.topic=connect-offsets
* offset.storage.replication.factor=-1
Which two statements match the expected behavior?
Select two.)
Answer: A,D
Explanation:
Apache Kafka Connect stores source connector offsets in a Kafka topic defined by the offset.storage.topic configuration. Since this property is explicitly set to connect-offsets, Kafka Connect will commit offsets to that topic, making Option C correct.
The offset.storage.replication.factor controls how many replicas the offsets topic will have. When this value is set to -1, Kafka Connect uses the broker's default replication factor, as documented in the Kafka Connect worker configuration reference. Therefore, Option A is also correct.
Option B is incorrect because Kafka Connect does not use Kafka's internal consumer offsets topic (__consumer_offsets). It always uses the configured offsets topic. Option D is incorrect because offset.flush.
timeout.ms defines how long Connect will wait for offset commits to complete, not how long it waits before attempting a commit. The commit interval itself is controlled by offset.flush.interval.ms (60 seconds in this case).
Thus, the correct statements that match the expected behavior are A and C.
NEW QUESTION # 41
You want to sink data from a Kafka topic to S3 using Kafka Connect. There are 10 brokers in the cluster, the topic has 2 partitions with replication factor of 3. How many tasks will you configure for the S3 connector?
Answer: D
Explanation:
You cannot have more sink tasks (= consumers) than the number of partitions, so 2.
NEW QUESTION # 42
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