Why schema management prevents data issues in Kafka - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to define a schema for a Kafka message.

Kafka

schema = {"type": [1], "fields": [{"name": "id", "type": "int"}]}

Drag options to blanks, or click blank then click option'

A"record"

B"string"

C"array"

D"map"

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'string' or 'array' instead of 'record' causes schema errors.

The schema type for Kafka messages is usually a record which defines a structured object.

2fill in blank

medium

Complete the code to register the schema with the Schema Registry.

Kafka

schema_registry_client.[1](schema_str)

Drag options to blanks, or click blank then click option'

Aregister_schema

Bget_schema

Cdelete_schema

Dupdate_schema

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'get_schema' does not add a new schema.

To add a new schema, you use register_schema method in the Schema Registry client.

3fill in blank

hard

Fix the error in the code to validate a message against the schema.

Kafka

is_valid = schema_validator.[1](message)

Drag options to blanks, or click blank then click option'

Averify_message

Bcheck_schema

Cvalidate_message

Dvalidate

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'validate_message' causes method not found errors.

The correct method to validate data against a schema is validate.

4fill in blank

hard

Fill both blanks to create a schema that requires a string field 'name' and an integer field 'age'.

Kafka

schema = {"type": [1], "fields": [{"name": "name", "type": [2], {"name": "age", "type": "int"}]}

Drag options to blanks, or click blank then click option'

A"record"

B"string"

C"int"

D"array"

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'array' or 'int' for the 'name' field causes validation errors.

The schema type is a record and the 'name' field type is string.

5fill in blank

hard

Fill all three blanks to create a schema with a required string 'email', an optional int 'phone', and a default value for 'phone'.

Kafka

schema = {"type": [1], "fields": [{"name": "email", "type": [2], {"name": "phone", "type": ["null", [3]], "default": null}]}

Drag options to blanks, or click blank then click option'

A"record"

B"string"

C"int"

D"array"

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'array' or wrong types causes schema validation failures.

The schema type is record, 'email' is a string, and 'phone' is an optional int with default null.