ARA-C01 · Question #84
A company has a source system that provides JSON records for various loT operations. The JSON Is loading directly into a persistent table with a variant field. The data Is quickly growing to 100s of…
The correct answer is A. Alter the target table to Include additional fields pulled from the JSON records. This would Include. The correct answer is A because it improves the performance of queries by reducing the amount of data scanned and processed. By adding a create_date field with a timestamp data type, Snowflake can automatically cluster the table based on this field and prune the…
Question
A company has a source system that provides JSON records for various loT operations. The JSON Is loading directly into a persistent table with a variant field. The data Is quickly growing to 100s of millions of records and performance to becoming an issue. There is a generic access pattern that Is used to filter on the create_date key within the variant field. What can be done to improve performance?
Options
- AAlter the target table to Include additional fields pulled from the JSON records. This would Include
- BAlter the target table to include additional fields pulled from the JSON records. This would include
- CValidate the size of the warehouse being used. If the record count is approaching 100s of millions,
- DIncorporate the use of multiple tables partitioned by date ranges. When a user or process needs to
How the community answered
(15 responses)- A80% (12)
- B7% (1)
- D13% (2)
Explanation
The correct answer is A because it improves the performance of queries by reducing the amount of data scanned and processed. By adding a create_date field with a timestamp data type, Snowflake can automatically cluster the table based on this field and prune the micro-partitions that do not match the filter condition. This avoids the need to parse the JSON data and access the variant field for every record.
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