Kafka for Developers - Data Contracts Using Schema Registry - Publish and Consumer Record Using Schema Registry

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Information Technology (IT), Architecture
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University
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Hard
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7 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the primary role of the Kafka Avro serializer in the producer setup?
To encrypt data for security
To compress data before sending
To interact with the Schema Registry and manage schema versions
To convert records into JSON format
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the purpose of changing the Kafka topic name to 'senior' in the producer setup?
To enable faster data processing
To avoid confusion with previous lectures
To improve data security
To enhance data compression
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which configuration is essential for the consumer to correctly parse records from the Schema Registry?
Kafka topic config
Kafka Avro serializer config
Schema registry URL config
Specific Avro reader config
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Why might there be a delay when publishing a record for the first time?
Due to network latency
Because the producer is waiting for consumer acknowledgment
Because the producer is establishing a connection with the Schema Registry
Due to data encryption processes
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the role of the schema version ID in the interaction with the Schema Registry?
To encrypt the data
To provide a timestamp for the record
To identify the schema used for a record
To compress the data
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What happens if the specific Avro reader config is not set in the consumer?
The consumer will treat records as generic data records
The consumer will not be able to read any records
The consumer will automatically set the config to true
The consumer will throw a network error
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
How can a consumer handle multiple event types being published to the same topic?
By setting the specific Avro reader config to false
By using a custom serializer
By treating records as generic records
By using a different Kafka topic for each event type
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