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| Section | Weight | Objectives |
|---|---|---|
| Schema Management | 5-10% | - Protobuf and JSON Schemas - Avro Schemas - Schema Registration and Retrieval - Schema Registry Architecture - Schema Evolution and Compatibility |
| Developing with Kafka Consumers | 15-20% | - Offset Management - Commit Strategies (Auto, Manual, Cooperative) - Consumer Interceptors - Deserialization - Consumer Groups and Rebalancing - Consumer Configuration and Tuning - Multi-threaded Consumers |
| Installing and Configuring Kafka | 5-10% | - Broker Configuration Parameters - Topic Creation and Configuration - Partition Assignment Strategies - ZooKeeper vs KRaft Mode |
| Kafka Connect | 10-15% | - Converters and Transforms - Single Message Transformations (SMT) - Connector Configuration - Source and Sink Connectors - Connect Architecture (Workers, Connectors, Tasks) - Offset Management in Connect |
| Monitoring and Operations | 5-10% | - Consumer Lag and Health Checks - Disaster Recovery - Cross-region Replication - Kafka Metrics and Monitoring - Log Compaction |
| Kafka Streams | 20-25% | - Streams Architecture and Topology - Testing Kafka Streams Applications - Windowed Operations (Tumbling, Hopping, Session) - Transformation Operations (map, filter, join, aggregate) - State Stores and Processors - Exactly-Once Processing - Interactive Queries |
| Kafka Fundamentals | 10-15% | - Topics, Partitions, and Offsets - Replication Factor and ISR - Log Segments and Retention - Producer and Consumer Basics - Brokers and Clusters |
| Security | 5-10% | - Authorization (ACLs) - Authentication (SASL, SSL/TLS) - Securing Schema Registry - Encryption in Transit |
| Developing with Kafka Producers | 15-20% | - Producer Interceptors - Exactly-Once Semantics - Serialization (JSON, Avro, Protobuf) - Partitioning Strategies - Error Handling and Retry Logic - Producer Configuration and Tuning - Idempotent Producers |
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NEW QUESTION # 89
To continuously export data from Kafka into a target database, I should use
Answer: B
Explanation:
Kafka Connect Sink is used to export data from Kafka to external databases and Kafka Connect Source is used to import from external databases into Kafka.
NEW QUESTION # 90
(You create a topic with five partitions.
What can you assume about messages read from that topic by a single consumer group?)
Answer: A
Explanation:
The Apache Kafka documentation explains that Kafka's parallelism model is based on topic partitions and consumer groups. Within a single consumer group, each partition can be assigned to at most one consumer at a time. As a result, the maximum level of parallelism for a consumer group is equal to the number of partitions in the topic.
In this scenario, the topic has five partitions, which means that up to five consumers in the same consumer group can actively consume data in parallel. If more than five consumers are added, the extra consumers will remain idle because there are no additional partitions to assign.
Option B is incorrect because Kafka does not enforce equal message consumption across partitions; message distribution depends on producers and partitioning strategy. Option C is false because partitions may be hosted on different brokers, and consumers fetch data from multiple brokers. Option D is incorrect because a single partition cannot be consumed by multiple consumers within the same group simultaneously.
Therefore, the only valid assumption is that a maximum of five consumers can actively consume messages in the same consumer group.
NEW QUESTION # 91
Match each configuration parameter with the correct option.
To answer choose a match for each option from the drop-down. Partial
credit is given for each correct answer.
Answer:
Explanation:
Explanation:
Correct Matches
* path.format#Only valid for some Source Connectors
* value.converter#Valid for all Source Connectors
* input.path#Only valid for some Source Connectors
* errors.retry.timeout#Valid for all Source Connectors
* tasks.max#Valid for all Source Connectors
* path.formatThis is a connector-specific configuration, commonly used by file-based or storage-based source connectors (for example, HDFS, S3, or FilePulse connectors). It isnot part of the Kafka Connect framework itself, so it is only valid for some source connectors.
* value.converterThis is aworker-level configurationthat defines how record values are converted (JSON, Avro, Protobuf, etc.). It applies toall connectorsrunning on the worker unless overridden at the connector level.
* input.pathThis configuration is specific to file-based source connectors (e.g., FileStreamSourceConnector). It isnot universally supported, so it is only valid for some source connectors.
* errors.retry.timeoutThis is part of Kafka Connect'serror handling framework, introduced to support retries and tolerance of transient errors. It is ageneric Connect configurationand valid for all connectors.
* tasks.maxThis is amandatory, generic connector configurationthat controls the maximum number of tasks a connector may create. It is valid forall source (and sink) connectors.
NEW QUESTION # 92
You are sending messages with keys to a topic. To increase throughput, you decide to increase the number of partitions of the topic. Select all that apply.
Answer: A,B
Explanation:
Increasing the number of partition causes new messages keys to get hashed differently, and breaks the guarantee "same keys goes to the same partition". Kafka logs are immutable and the previous messages are not re-shuffled
NEW QUESTION # 93
(You are writing to a Kafka topic with producer configuration acks=all.
The producer receives acknowledgements from the broker but still creates duplicate messages due to network timeouts and retries.
You need to ensure that duplicate messages are not created.
Which producer configuration should you set?)
Answer: D
Explanation:
The official Apache Kafka producer documentation states that setting enable.idempotence=true guarantees that messages are written to a partition exactly once, even in the presence of retries caused by network failures or broker timeouts. This feature prevents duplicate records by assigning producer sequence numbers and validating them on the broker side.
For idempotent producers to work correctly, retries must be enabled, which is why Kafka recommends a very large value such as retries=2147483647. Additionally, limiting max.in.flight.requests.per.connection to 1 ensures strict ordering during retries, preventing message reordering in older Kafka versions and providing the safest configuration.
Option A is irrelevant to producers. Option B explicitly disables idempotence, which causes duplicates.
Option D disables retries, which increases the risk of message loss.
Therefore, the correct and fully documented solution to eliminate duplicate messages is enabling idempotence with retries and a safe in-flight request limit, as shown in Option C.
NEW QUESTION # 94
......
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