You have identified a valuable dataset on the Snowflake Marketplace related to weather patterns. To consume this data, you perform the following actions: 1. You request and receive the data share from the provider. 2. You create a database named 'WEATHER DB' from the share. Now you want to create a secure view named 'DAILY WEATHER SUMMARY in your own database 'ANALYTICS DB.PUBLIC', which joins your internal sales data C ANALYTICS DB.PUBLIC.SALES) with the weather data from the provider's 'WEATHER DB.WEATHER SCHEMA.DAILY WEATHER' table. You only want to expose specific columns from both tables in your view to minimize data exposure. Which of the following steps are required to ensure this secure and functional integration?

• A.Grant 'SELECT privileges on 'ANALYTICS DB.PUBLIC.SALES' to the share provider.
• B.No additional steps are required; you can directly query in your view definition.
• C.Create an outbound share and grant usage on the ANALYTICS DB database
• D.Create the view using fully qualified names, selecting only the necessary columns from both the 'SALES' table and the 'DAILY WEATHER table.
• E.Create a warehouse with access control policies enabled.

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In diagnostic analysis, what significance do demographics and relationships hold when identifying anomalies? (Select all that apply)

• A.Considering relationships among data variables
• B.Identifying demographic variations linked to anomalies
• C.Analyzing only recent demographic data for anomalies
• D.Ignoring data relationships for focused analysis

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You are working with a Snowflake table 'raw_data' containing a column of type TEXT that stores log messages in various formats, including JSON and CSV. You need to extract specific data points from these log entries, treating JSON entries differently from CSV entries. Specifically: For JSON log entries (identified by starting with '{l), extract the value of the 'user_id' key. For CSV log entries (identified by starting with a number), extract the second field (assuming comma-separated values). Which of the following queries is the most efficient and correct way to achieve this data extraction?

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

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You are preparing to load data from an internal stage that contains multiple CSV files into a Snowflake table. You need to ensure that only files matching a specific pattern are loaded (e.g., files starting with 'sales_'). You also need to transform the date column (located in the third position of each CSV file) from 'YYYYMMDD' to 'YYYY-MM-DD' format during the load. The target table 'SALES has columns 'product_id', and 'region'. Which combination of options correctly filters the files and transforms the date format during the load process?

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

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You are analyzing customer order data in Snowflake and need to determine if there is a statistically significant correlation between the number of items in an order ('ITEM COUNT) and the total order value CORDER VALUE'). You have a table named 'ORDERS' with columns 'ORDER ID', 'ITEM COUNT', and 'ORDER VALUE'. Which of the following Snowflake functions or methods, used in combination, would be the MOST appropriate and statistically sound for calculating the correlation coefficient between these two variables, taking into account the need to handle potential NULL values appropriately?

• A.Use the 'CORR function directly on the 'ITEM_COUNT and 'ORDER_VALUE columns. No special handling is needed for NULL values as 'CORR automatically ignores them.
• B.First, replace NULL values in both 'ITEM COUNT and 'ORDER _ VALUE with 0 using 'COALESCE, then apply the 'CORR function.
• C.Use 'AVG' and 'STDDEV_POP functions to calculate the mean and standard deviation for both and Then, manually compute the correlation coefficient using these statistics.
• D.Use 'QUALIFY Clause with 'CORR function to determine the coefficient values, and then apply statistical significance tests manually by calculating p-values.
• E.Use a combination of 'WHERE clause to filter out rows where either 'ITEM COUNT or 'ORDER VALUE is NULL, and then apply the 'CORR function to the filtered data.

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