SOL-C01 · Question #170
You are designing a data ingestion pipeline in Snowflake using Snowsight to load JSON data from an external stage (AWS S3). The JSON files contain nested arrays and objects, and you need to flatten th
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Question
You are designing a data ingestion pipeline in Snowflake using Snowsight to load JSON data from an external stage (AWS S3). The JSON files contain nested arrays and objects, and you need to flatten the data and load specific fields into a relational table 'FLATTENED DATA' with columns , 'NAME', 'VALUE', and 'TIMESTAMP'. Given the sample JSON structure below, which of the following COPY INTO statement, combined with an appropriate file format, would BEST achieve this goal without requiring a complex transformation process after loading?
Options
- AUse the = TRUE' file format option and `COPY INTO' with a 'SELECT statement that uses
- BLoad the entire JSON file into a VARIANT column in a staging table and then use a separate query
- CCreate an external table pointing to the JSON files and then use a 'CREATE TABLE AS SELECT
- DUse a file format with ` TYPE = JSON' and = TRUE (if the JSON data is enclosed in an array). In
- ELoad the JSON files directly into the FLATTENED_DATX table without flattening and rely on
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