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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions:

1. You have a table named 'TRANSACTIONS with the following definition: CREATE TABLE TRANSACTIONS ( TRANSACTION ID NUMBER, TRANSACTION DATE DATE, CUSTOMER_ID NUMBER, AMOUNT PRODUCT_CATEGORY VARCHAR(50) Users frequently query this table using filters on both 'TRANSACTION_DATE and 'PRODUCT CATEGORY. You want to optimize query performance. What is the MOST effective approach?

A) Create separate indexes on 'TRANSACTION DATE' and 'PRODUCT CATEGORY.
B) Partition the table by 'TRANSACTION DATE
C) Cluster the table using a composite key of '(TRANSACTION_DATE, PRODUCT CATEGORY)'.
D) Cluster the table on ' TRANSACTION_DATE and then create a materialized view filtered by PRODUCT_CATEGORY&.
E) Create a materialized view joining 'TRANSACTIONS' with a dimension table containing product category information.

2. You are using Snowpark Python to perform a complex data transformation involving multiple tables and several intermediate dataframes. During the transformation, an error occurs within one of the Snowpark functions, causing the entire process to halt. To ensure data consistency, you need to implement transaction management. Which of the following Snowpark DataFrameWriter options or session configurations would be MOST appropriate for rolling back the entire transformation in case of an error during the write operation to the final target table?

A) Wrap the entire transformation in a stored procedure and call 'SYSTEM$QUERY within the stored procedure's exception handler.
B) Set the session parameter to 'TRUE and wrap the entire transformation within a 'try...except block, explicitly calling in the 'excepts block.
C) Set the session parameter to 'TRUE to ensure all DDL operations are atomic and can be rolled back.
D) Use and manually track intermediate dataframes to delete them in case of failure.
E) Use True)' to automatically rollback the write operation if an error occurs during the write process.

3. You have a Snowflake table named 'CUSTOMER DATA that contains sensitive Personally Identifiable Information (PII). You need to grant a data analyst access to a subset of the data while masking specific columns containing PII. Which of the following Snowflake features, when used in combination, provides the MOST secure and efficient solution?

A) Create a secure view on 'CUSTOMER DATA' with column masking policies and grant SELECT privileges on the secure view.
B) Create a new table containing only the non-sensitive columns and grant SELECT privileges on this table.
C) Create a view on 'CUSTOMER_DATA' with column masking policies and grant SELECT privileges on the view.
D) Grant the analyst the 'SELECT privilege on the 'CUSTOMER_DATA' table and implement row-level security based on the analyst's role.
E) Create a stored procedure that retrieves the data with masking applied and grant the analyst EXECUTE privilege on the stored procedure.

4. You're designing a data masking solution for a 'CUSTOMER' table with columns like 'CUSTOMER ID', 'NAME', 'EMAIL', and 'PHONE NUMBER. You want to implement the following requirements: 1. The 'SUPPORT' role should be able to see the last four digits of the 'PHONE NUMBER and a hashed version of the 'EMAIL'. 2. The 'MARKETING' role should be able to see the full 'NAME' and a domain-only version of the 'EMAIL' (everything after the '@' symbol). 3. All other roles should see masked values for 'EMAIL' and 'PHONE NUMBER. Which of the following masking policy definitions BEST achieves these requirements using Snowflake's built-in functions and RBAC?

A)

B)

C)

D)

E)

5. Consider the following Snowflake SQL API call to execute a stored procedure:

A) Include the stored procedure's fully qualified name (database.schema.procedure_name) in the 'statement' parameter.
B) The stored procedure should handle the error handling for network disruptions and automatically retry.
C) Use the parameter to specify which external functions are allowed to be called by the procedure.
D) Set the 'warehouse' parameter in the SQL API request to ensure the stored procedure uses a specific warehouse size.
E) Set the parameter to and retrieve the result set directly from the API response.

Solutions: