Data Scientist used streams in ELT (extract, load, transform) processes where new data inserted in-to a staging table is tracked by a stream. A set of SQL statements transform and insert the stream contents into a set of production tables. Raw data is coming in the JSON format, but for analysis he needs to transform it into relational columns in the production tables. which of the following Data transformation SQL function he can used to achieve the same?
To know about lateral flatten SQL Function, please refer:
https://docs.snowflake.com/en/sql-reference/constructs/join-lateral#example-of-using-lateral-with-flatten
Mark the correct steps for saving the contents of a DataFrame to a Snowflake table as part of Moving Data from Spark to Snowflake?
Moving Data from Spark to Snowflake
The steps for saving the contents of a DataFrame to a Snowflake table are similar to writing from Snowflake to Spark:
1. Use the write() method of the DataFrame to construct a DataFrameWriter.
2. Specify SNOWFLAKE_SOURCE_NAME using the format() method.
3. Specify the connector options using either the option() or options() method.
4. Use the dbtable option to specify the table to which data is written.
5. Use the mode() method to specify the save mode for the content.
Examples
1. df.write
2. .format(SNOWFLAKE_SOURCE_NAME)
3. .options(sfOptions)
4. .option('dbtable', 't2')
5. .mode(SaveMode.Overwrite)
6. .save()
Consider a data frame df with columns ['A', 'B', 'C', 'D'] and rows ['r1', 'r2', 'r3']. What does the ex-pression df[lambda x : x.index.str.endswith('3')] do?
It will Filters the row labelled r3.
Which of the following is a useful tool for gaining insights into the relationship between features and predictions?
Partial dependence plots (PDP) is a useful tool for gaining insights into the relationship between features and predictions. It helps us understand how different values of a particular feature impact model's predictions.
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