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Snowflake Certified SnowPro Associate - Platform Certification Sample Questions (Q60-Q65):

NEW QUESTION # 60
You're writing a Snowflake Notebook that needs to dynamically create tables based on a list of table names stored in a Python list called 'table_names'. You want to iterate through this list and execute a CREATE TABLE statement for each table name. However, the names might include spaces and special characters. Which approach or approaches can you use to achieve this goal safely and effectively?

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

Answer: C,E

Explanation:
Options A and D are correct. Option A uses double quotes to safely enclose the table name within the CREATE TABLE statement. This is the standard way to handle identifiers with spaces or special characters in SQL. Option D provides a Snowpark DataFrame-centric approach that automatically handles identifier quoting when creating the table using `saveAsTable' . Option B modifies the table names by replacing spaces. Option C's reliance on shell escaping ('shlex.quote') is not relevant or necessary when executing SQL within a Snowpark session.
Option E is unsafe as it does not handle the case where table names have spaces or other special characters and will result in SQL errors.

NEW QUESTION # 61
You have created a database 'reporting_db' with the role 'reporting_admin'. You now want to transfer the ownership of this database to a different role, Which of the following SQL statement or statements will correctly transfer ownership of the to 'security admin'?

Answer: E

Explanation:
The correct way to transfer database ownership in Snowflake is using the 'ALTER DATABASE ...
TRANSFER OWNERSHIP TO ROLE ...s command. `GRANT OWNERSHIP' only grants the privilege but doesn't transfer ownership. The syntax in options C, D and E are invalid in Snowflake for transferring ownership.

NEW QUESTION # 62
You have a Snowflake table named `CUSTOMER DATA' with columns `CUSTOMER ID', `NAME,
'CITY, and 'TRANSACTION DATE. You need to implement a data masking policy that masks the
'NAME column for all users except those with the 'ACCOUNTADMIN' role. Additionally, you want to implement a row-level security policy that filters the data based on the `CITY column, allowing users with the 'CITY MANAGER role to only see data for their assigned city. How would you implement these policies?

A. Create a view on top of the 'CUSTOMER_DATA' table and implement the masking and row-level security logic within the view definition using CASE statements based on CURRENT ROLE() and CURRENT USER().
B. Create masking policy using the 'TRANSFORM' function to mask the 'NAME' column, and create a row access policy to filter rows based on 'CITY'. Ensure the row access policy uses a mapping table between CITY_MANAGER and CITY.
C. Create a stored procedure with 'EXECUTE AS OWNER to filter the data based on the 'CITY column for certain users, and masking policy to use `SHA256' on the 'NAME column
D. Create a masking policy using regular expressions to filter the data. Create a stored procedure to filter the data based on the 'CITY' column for certain users.
E. Create a masking policy for the `NAME' column using a CASE statement that checks the CURRENT ROLE() and applies the mask if the role is not ACCOUNTADMIW. create a security policy for row-level filtering on the 'CITY column, checking the and to determine the appropriate filter condition. Apply the row access policy to the table.

Answer: B,E

Explanation:
Options A and D correctly implements masking and row-level security. Option A utilizes masking policy for the 'NAME column that considers the `ACCOUNTADMIN' role. And utilizes a row-level security policy (row access policy) that consider 'CITY_MANAGER and 'CITY' for filtering. Option D utilizes `TRANSFORM' to transform and mask the 'NAME' column and implements row access policy which includes Mapping table between CITY and CITY_MANAGER. Option B,C, and E are incorrect as they either create a view, stored procedure and/or regular expressions, which are not the best practices of implementing masking and row-level security in Snowflake.

NEW QUESTION # 63
You have a JSON file stored in an internal stage containing customer data with varying schem

A. Use a stored procedure to iterate through each JSON record, determine the address format, and then extract the address.
B. Some customers have address information stored as a nested JSON object, while others have it stored as a simple string. How can you efficiently query all customer records and retrieve the address information, regardless of its format?
C. Load the JSON data into a relational table and use a 'UNION ALL' query to combine records with different schema.
D. Use to dynamically determine the column type and then apply appropriate conversion functions.
E. Use the `LATERAL FLATTEN' function to explode the JSON and then use and to handle different formats.
F. Create two different views: one for customers with nested address objects and another for customers with string addresses. Then use 'IJNION ALL' on the views.

Answer: B

Explanation:
'LATERAL FLATTEN' helps process JSON and then the and functions are used to handle the different formats of the address. This is the most efficient approach. Loading into a relational table is not suited for semi-structured data. Using two views then UNION ALL is a valid strategy, however Flatten will be more efficient. Using a stored procedure would be inefficient.

NEW QUESTION # 64
You're using a Snowflake Notebook to collaboratively develop a data pipeline. Several team members are working on the same notebook concurrently. One team member accidentally deletes a cell containing critical data transformation logic. What Snowflake features, accessible within or through the Notebook environment, can be used to recover the lost cell's content and minimize disruption to the workflow? Select TWO correct answers.

A. Examine the Snowflake query history associated with the notebook session. The SQL statements executed in the deleted cell may be present in the query history, allowing you to reconstruct the code.
B. Check the Snowflake Notebook's version history (if enabled) to revert to a previous version of the notebook before the cell was deleted.
C. Since Snowflake Notebooks automatically back up cell contents to a secure cloud storage location, contact Snowflake support to request a restoration of the deleted cell.
D. Leverage the Snowflake Time Travel feature on the underlying table(s) used in the deleted cell's logic to retrieve the data at a point in time before the deletion occurred, then recreate the cell with the recovered data transformation logic.
E. Use the 'Undo' function within the Snowflake Notebook editor to revert the deletion. If the 'Undo' history is insufficient, manually re-enter the code from memory or documentation.

Answer: A,B

Explanation:
Snowflake Notebook version history (C) is the primary mechanism for reverting to a previous state. The Snowflake query history (D) will contain the SQL commands executed by the cell, allowing for reconstruction of the logic. 'Undo' (A) is limited and may not be sufficient. Time Travel (B) applies to data , not code in the notebook. Snowflake does not automatically back up individual cells for restoration (E).

NEW QUESTION # 65
......

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