CCDAK Prüfung - CCDAK Testfagen

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Confluent CCDAK Exam Syllabus Topics:

Section	Weight	Objectives
Kafka Fundamentals	10-15%	- Replication Factor and ISR - Topics, Partitions, and Offsets - Log Segments and Retention - Producer and Consumer Basics - Brokers and Clusters
Installing and Configuring Kafka	5-10%	- ZooKeeper vs KRaft Mode - Partition Assignment Strategies - Topic Creation and Configuration - Broker Configuration Parameters
Monitoring and Operations	5-10%	- Cross-region Replication - Kafka Metrics and Monitoring - Consumer Lag and Health Checks - Disaster Recovery - Log Compaction
Kafka Streams	20-25%	- Exactly-Once Processing - Streams Architecture and Topology - State Stores and Processors - Interactive Queries - Windowed Operations (Tumbling, Hopping, Session) - Testing Kafka Streams Applications - Transformation Operations (map, filter, join, aggregate)
Schema Management	5-10%	- Schema Registry Architecture - Protobuf and JSON Schemas - Schema Evolution and Compatibility - Avro Schemas - Schema Registration and Retrieval
Security	5-10%	- Securing Schema Registry - Authorization (ACLs) - Authentication (SASL, SSL/TLS) - Encryption in Transit
Developing with Kafka Producers	15-20%	- Partitioning Strategies - Producer Configuration and Tuning - Serialization (JSON, Avro, Protobuf) - Exactly-Once Semantics - Idempotent Producers - Error Handling and Retry Logic - Producer Interceptors
Developing with Kafka Consumers	15-20%	- Consumer Interceptors - Commit Strategies (Auto, Manual, Cooperative) - Consumer Configuration and Tuning - Offset Management - Deserialization - Consumer Groups and Rebalancing - Multi-threaded Consumers
Kafka Connect	10-15%	- Converters and Transforms - Offset Management in Connect - Connect Architecture (Workers, Connectors, Tasks) - Single Message Transformations (SMT) - Source and Sink Connectors - Connector Configuration

>> CCDAK Prüfung <<

Confluent CCDAK: Confluent Certified Developer for Apache Kafka Certification Examination braindumps PDF & Testking echter Test

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Confluent Certified Developer for Apache Kafka Certification Examination CCDAK Prüfungsfragen mit Lösungen (Q60-Q65):

60. Frage
A client connects to a broker in the cluster and sends a fetch request for a partition in a topic. It gets an exception Not Leader For Partition Exception in the response. How does client handle this situation?

A. Get the Broker id from Zookeeper that is hosting the leader replica and send request to it
B. Send metadata request to the same broker for the topic and select the broker hosting the leader replica
C. Send fetch request to each Broker in the cluster
D. Send metadata request to Zookeeper for the topic and select the broker hosting the leader replica

Antwort: B

Begründung:
In case the consumer has the wrong leader of a partition, it will issue a metadata request. The Metadata request can be handled by any node, so clients know afterwards which broker are the designated leader for the topic partitions. Produce and consume requests can only be sent to the node hosting partition leader.

61. Frage
The producer code below features a Callback class with a method called onCompletion().
In the onCompletion() method, when the request is completed successfully, what does the value metadata.
offset() represent?

A. The sequential ID of the message committed into a partition
B. The number of bytes that overflowed beyond a producer batch of messages
C. The ID of the partition to which the message was committed
D. Its position in the producer's batch of messages

Antwort: A

Begründung:
The offset in the RecordMetadata object returned by the producer represents the position of the record in the partition - i.e., the sequential ID assigned by Kafka once the message is committed.
From Kafka Producer API Documentation:
"The offset is the position of the record in the partition. This is a unique, sequential number assigned by the broker." D refers to metadata.partition(), not offset().
B and C are unrelated to how Kafka handles committed offsets.
Reference: Kafka Producer Java API > RecordMetadata

62. Frage
is KSQL ANSI SQL compliant?

A. Yes
B. No

Antwort: B

Begründung:
KSQL is not ANSI SQL compliant, for now there are no defined standards on streaming SQL languages

63. Frage
(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?)

A. POST /connectors/s3-connector/restart?includeTasks=true
B. POST /connectors/s3-connector/tasks/0/restart
C. POST /connectors/s3-connector/restart?includeTasks=true&onlyFailed=true
D. POST /connectors/s3-connector/restart

Antwort: A

Begründung:
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.

64. Frage
If I produce to a topic that does not exist, and the broker setting auto.create.topic.enable=true, what will happen?

A. Kafka will automatically create the topic with num.partitions=#of brokers and replication.factor=3
B. Kafka will automatically create the topic with the broker settings num.partitions and default.replication.
factor
C. Kafka will automatically create the topic with 1 partition and 1 replication factor
D. Kafka will automatically create the topic with the indicated producer settings num.partitions and default.
replication.factor

Antwort: B

Begründung:
The broker settings comes into play when a topic is auto created

65. Frage
......

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