You have data residing in AWS S3 in Parquet format, which is updated daily with new columns being added occasionally. The data is rarely accessed, but when it is, it needs to be queried using SQL within Snowflake. You want to minimize storage costs within Snowflake while ensuring the data can be queried without requiring manual table schema updates every time a new column is added to the S3 data'. Which approach is MOST suitable?

• A. Option D
• B. Option E
• C. Option C
• D. Option A
• E. Option B

You've created a JavaScript stored procedure using Snowpark to transform data'. The stored procedure is failing, and you suspect an issue with how Snowpark is handling null values during a join operation. Given two Snowpark DataFrames, and 'df2 , what is the expected behavior when performing an inner join on a column containing null values in both DataFrames, and how can you mitigate potential issues?

• A. The inner join will exclude rows where the join column is null in either DataFrame. To include these rows, you must use a full outer join instead.
• B. The inner join will automatically exclude rows where the join column is null in either DataFrame. There is no need for explicit null handling.
• C. The inner join will treat null values as equal, resulting in rows where the join column is null in both DataFrames being included in the result. To avoid this, you should filter out null values before the join.
• D. Inner Join will not throw an error, and will exclude the rows where join column is null. If you need to join records with null values, pre-processing dataframes using to replace null with a valid sentinel value before performing the join is one way to handle this.
• E. The behavior of the inner join with null values is undefined and may vary depending on the data types and the specific version of Snowpark. Explicit null handling is always required.

A data engineering team is using a Snowflake stream to capture changes made to a source table named 'orders'. They want to only capture 'INSERT and 'UPDATE operations but exclude 'DELETE operations from being captured in the stream. Which of the following configurations will achieve this requirement? Assume the stream has already been created and is named 'orders_stream'.

• A. Alter the stream using the 'HIDE_DELETES parameter: 'ALTER STREAM orders_stream SET HIDE_DELETES = TRUE;'
• B. Create a view on top of the base table that filters out deleted rows, and then create a stream on the view.
• C. It's impossible to configure a stream to exclude specific DML operations. All changes are always tracked.
• D. Create a Snowflake task that periodically truncates the stream's metadata table, removing DELETE records.
• E. Use task and stream combination. In the task, create view using 'select from orders where metadata$isDelete = false' and create stream on that view.

You are managing a Snowflake environment where data retention is set to the default 1 day for all databases and tables. You need to clone a production table, 'CUSTOMER DATA, to a development environment to test some complex transformations. However, after cloning, you realize that the original 'CUSTOMER DATA' table in production was accidentally dropped 2 days ago. Which of the following statements accurately describe the situation and what can be done, if anything?

• A. The cloned 'CUSTOMER DATA' table in the development environment will inherit the data retention period from the original 'CUSTOMER DATA' table, so it will also be inaccessible after 1 day if dropped in development.
• B. The cloned 'CUSTOMER DATA' table in the development environment will no longer be accessible because the source table has been dropped and the data retention period has expired. The clone is effectively useless.
• C. The cloned 'CUSTOMER DATA' table will only remain accessible if the source database was also cloned at the same time. If only the table was cloned, the cloned table is unusable after 1 day.
• D. The cloned 'CUSTOMER DATA' table in the development environment will remain accessible because the clone operation created a point-in-time snapshot.
• E. While the source 'CUSTOMER DATA' is dropped and outside the retention period, the clone created will remain available and fully functional for querying. It is an independent copy. The clone is usable indefinitely, within the limits of your Snowflake account storage.