SOL-C01 · Question #211
You're building a data pipeline in Snowflake that utilizes the 'SNOWFLAKE.ML.COMPLETE function to translate product descriptions from English to French. You have a table 'PRODUCT DESCRIPTIONS with…
The correct answer is A. Option A. The most efficient and correct approach is to use a SQL UDF, as it leverages Snowflake's internal optimization capabilities best when calling built-in functions. The `SNOWFLAKE.ML.COMPLETE is called directly within the SQL UDE Options B and C are incorrect because Javascript…
Question
You're building a data pipeline in Snowflake that utilizes the 'SNOWFLAKE.ML.COMPLETE function to translate product descriptions from English to French. You have a table 'PRODUCT DESCRIPTIONS with columns 'PRODUCT and ENGLISH DESCRIPTION'. You want to create a new table 'PRODUCT DESCRIPTIONS FRENCH' with 'PRODUCT and 'FRENCH DESCRIPTION'. To optimize costs, you plan to use a efficient way to implement this, including the UDF definition and the data loading process?
Exhibit
Options
- AOption A
- BOption B
- COption C
- DOption D
- EOption E
How the community answered
(41 responses)- A78% (32)
- B12% (5)
- C7% (3)
- E2% (1)
Explanation
The most efficient and correct approach is to use a SQL UDF, as it leverages Snowflake's internal optimization capabilities best when calling built-in functions. The `SNOWFLAKE.ML.COMPLETE is called directly within the SQL UDE Options B and C are incorrect because Javascript and Python UDF would need proper Snowflake SDK package (snowflake.ml.complete) to work in order to call SNOWFLAKE.ML.COMPLETE function; Option D is incorrect; Option E is also not optimal; Option A is the MOST efficient method since it uses a SQL UDF to directly call the function which is better optimised by Snowflake.
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