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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. Consider the following Snowflake SQL statement intended to modify the properties of a Snowpark-optimized virtual warehouse named
'SNOWPARK_WH':

Which of the following statements accurately describe the expected outcome of executing this SQL statement?

A) The SQL statement will fail because the 'SCALING POLICY parameter cannot be set for Snowpark-optimized warehouses.
B) The SQL statement will execute successfully only if the user executing it has the 'MODIFY privilege on the 'SNOWPARK_WH' warehouse.
C) The SQL statement will fail because you cannot modify 'WAREHOUSE_SIZE and 'MAX CLUSTER COUNT in a single SALTER WAREHOUSE statement.
D) The SQL statement will execute successfully, resizing the 'SNOWPARK WI-i' warehouse to 'LARGE', setting the maximum number of clusters to 3, the minimum to 1, and enabling the 'ECONOMY' scaling policy.
E) The SQL statement will execute successfully after checking if 'SNOWPARK_WH' is of Snowpark-optimized warehouse type, resizing the 'SNOWPARK_WH' warehouse to 'LARGE', setting the maximum number of clusters to 3, the minimum to 1, and enabling the 'ECONOMY' scaling policy.

2. Consider a scenario where you're developing a Snowpark stored procedure that accesses sensitive data'. Which of the following strategies, when used together, provide a comprehensive approach to secure this stored procedure and protect the underlying data?
Select all that apply:

A) Using 'EXECUTE AS OWNER and granting the 'SELECT privilege on the sensitive data tables to the stored procedure's owner role.
B) Using 'EXECUTE AS CALLER and relying on the caller's privileges to access the data.
C) Implementing row-level security policies on the sensitive data tables.
D) Encrypting the stored procedure's code using AES encryption before deployment.
E) Masking sensitive data within the stored procedure using Snowflake's dynamic data masking policies.

3. Consider a Snowpark DataFrame with a containing date values, some of which are corrupted (e.g., invalid date formats or out-of-range values). You need to identify and either remove or correct these corrupted date values. Which of the following approaches can be effectively used in Snowpark Python to handle such scenarios? (Select all that apply)

A) Define a UDF that attempts to parse the date string and returns a valid date or None if parsing fails; then, use this UDF to create a new column and filter rows with None values.
B) Use the 'to_date' function with the 'IGNORE' keyword to automatically replace invalid dates with NULL.
C) Directly replace corrupt dates using df.na.fill() after identifying the corrupt dates.
D) Use function to convert the column and filter out rows where the conversion results in NULL.
E) Implement a regular expression-based filter to remove rows where the 'date_column' does not match a valid date format.

4. A Snowpark application needs to process large volumes of sensor data stored in a Snowflake table named , which includes columns , 'timestamp' , and The application must calculate a rolling average of for each over a 5-minute window. The data is not perfectly ordered by 'timestamp' within each 'sensor_id'. What is the MOST efficient and accurate way to implement this rolling average calculation using Snowpark?

A) Using after applying a filter to select only the data within the 5-minute window, updating the filter for each new window.
B) Implementing a Python UDTF (User-Defined Table Function) that iterates through the data for each calculates the rolling average manually, and emits the results as rows.
C) Using a Window specification with 'orderBy('timestamp')' and 'rowsBetween(Window.unboundedPreceding, Window.currentRow)' to calculate the cumulative average, then subtracting the average from 5 minutes ago. The query will then be grouped on the sensor id.
D) Using a Window specification with 'orderBy('timestamp')' and 'rowsBetween(Window.unboundedPreceding, Window.currentRow)' in conjunction with and a UDF to manually calculate the rolling average within each group.
E) Using a Window specification with 0)' and the 'avg()' window function. (Where 'to_seconds' converts a duration to seconds)

5. You are using VS Code with the Snowflake extension to develop a Snowpark application. You have successfully connected to your Snowflake account and are writing a script that creates a stage and then loads data from a local file into a Snowflake table using Snowpark. However, you're encountering issues with file paths and permission errors. Which of the following strategies would best address these challenges and ensure your Snowpark application can reliably load data from local files?

A) Use absolute file paths in your Snowpark code when referring to local files. Ensure the Snowflake service account has read access to the local file system.
B) Use VS Code's remote development feature to run your Snowpark code directly on the Snowflake compute nodes. This will eliminate file path issues.
C) Leverage a network share and mount it as a drive in both your local development environment and the Snowflake environment. Then, use relative file paths in your Snowpark code.
D) Utilize Snowpark's 'session.file.put' to upload the local file to an internal or external stage. Then, use 'session.table.copy_into' to load data from the stage into the target table.
E) Modify the Snowflake account-level parameters to allow direct access to the local file system. Use relative file paths to access the local file.

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