When unloading data, which combination of parameters should be used to differentiate between empty strings and NULL values? (Select TWO).
When unloading data in Snowflake, it is essential to differentiate between empty strings and NULL values to preserve data integrity. The parameters FIELD_OPTIONALLY_ENCLOSED_BY and EMPTY_FIELD_AS_NULL are used together to address this:
FIELD_OPTIONALLY_ENCLOSED_BY: This parameter specifies the character used to enclose fields, which can differentiate between empty strings (as enclosed fields) and NULLs.
EMPTY_FIELD_AS_NULL: By setting this parameter, Snowflake interprets empty fields as NULL values when unloading data, ensuring accurate representation of NULLs versus empty strings.
These parameters are crucial when exporting data for systems that need explicit differentiation between NULL and empty string values.
When unloading data, which file format preserves the data values for floating-point number columns?
When unloading data, the Parquet file format is known for its efficiency in preserving the data values for floating-point number columns. Parquet is a columnar storage file format that offers high compression ratios and efficient data encoding schemes. It is especially effective for floating-point data, as it maintains high precision and supports efficient querying and analysis.
References:
Snowflake Documentation: Using the Parquet File Format for Unloading Data
Which snowflake objects will incur both storage and cloud compute charges? (Select TWO)
In Snowflake, both materialized views and transient tables will incur storage charges because they store data. They will also incur compute charges when queries are run against them, as compute resources are used to process the queries. References: [COF-C02] SnowPro Core Certification Exam Study Guide
What is the MINIMUM Snowflake edition required to use the query acceleration service?
Why is a federated environment used for user authentication in Snowflake?
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