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A data engineer is using the Snowflake Spark connector to read a large table from Snowflake into a Spark DataFrame. The table contains a 'TIMESTAMP NTT column. After loading the data, the engineer observes that the values in the 'TIMESTAMP NTZ' column are not preserved accurately when retrieved from the DataFrame. What are the potential issues and what configurations can be adjusted in Snowflake to improve the result?
Correct Answer: B,D
Explanation: Option B identifies a key cause: timezone mismatches between Spark and Snowflake. Ensuring these are aligned is essential. Option D proposes converting 'TIMESTAMP NTZ to strings using 'sfTimestampNTZAsString' is the correct approach to preserve precise values, and setting the Spark session timezone is also critical. Option A is incorrect since there are parameters. Option C involves unnecessary data transformation within Snowflake which is what we are trying to avoid with the connector. Setting 'sfTimestampNTZAsString' to false causes conversion which lead to data issue.