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

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

2. You are using Snowpark Python to transform a DataFrame 'df_orderS containing order data'. You need to filter the DataFrame to include only orders with a total amount greater than $1000 and placed within the last 30 days. Assume the DataFrame has columns 'order_id', 'order_date' (timestamp), and 'total_amount' (numeric). Which of the following code snippets is the MOST efficient and correct way to achieve this filtering using Snowpark?

A) Option B
B) Option D
C) Option C
D) Option A
E) Option E

3. You are designing a data pipeline in Snowflake to process IoT sensor data'. The data arrives in JSON format, and you need to extract specific nested fields using a Snowpark UDF for performance reasons. Which of the following statements are true regarding best practices and limitations when working with complex JSON data and Snowpark UDFs (Python or Scala)? (Select all that apply)

A) For highly complex JSON structures, consider using a Scala UDF with a robust JSON parsing library like Jackson or Gson for potentially better performance and control over error handling compared to Python UDFs.
B) Ensure the UDF is idempotent, meaning it produces the same output for the same input, as Snowflake might execute UDFs multiple times for optimization purposes.
C) The maximum size of the JSON document that can be processed by a Snowpark UDF is directly limited by the maximum size of the UDF code itself (typically a few MB), requiring chunking strategies for large JSON payloads.
D) Leverage Snowflake's built-in 'PARSE_JSON' function and 'GET_PATH' function outside of the UDF as much as possible before passing the data to the UDF to reduce the complexity within the UDF itself.
E) When working with Snowpark Python UDFs, it's recommended to use the 'json' module in Python to parse the JSON data within the UDF, as it's optimized for Snowflake's internal JSON representation.

4. You are designing a data protection strategy for a Snowflake environment that processes sensitive payment card industry (PCI) data'. You decide to use a combination of column-level security and external tokenization. Which of the following statements are TRUE regarding the advantages of using both techniques together? (Select TWO)

A) Column-level security can be used to restrict access to the tokenization UDF itself, ensuring that only authorized users can perform tokenization or detokenization operations.
B) Tokenization ensures compliance with PCl DSS standards, while masking policies are primarily useful for internal access control and obfuscation for development environments. Using both doesn't increase security.
C) Masking policies and external tokenization provide independent layers of security. If one is compromised, the other still provides protection.
D) Combining masking policies and external tokenization allows for complete elimination of PCl data from the Snowflake environment, even during processing.
E) The use of both techniques increases query performance drastically.

5. You are performing a series of complex data transformations on a large table named 'TRANSACTIONS' in Snowflake. After running several DML statements, you realize that an earlier transformation step introduced incorrect data into the table. You want to rollback the table to a state before that specific transformation occurred. Which of the following methods could be used to achieve this rollback, assuming you know the exact timestamp or query ID of the state you want to revert to? Select all that apply.

A) Use Time Travel to query the historical version of the 'TRANSACTIONS' table using the 'AT' or 'BEFORE clause with either the timestamp or query ID. Then, use 'INSERT OVERWRITES or ' REPLACE TABLES statement to replace the current content of the original table with the historical data.
B) Use the UNDROP TABLE command if the table was dropped accidentally, then manually re-apply the correct transformations.
C) Create a clone of the ' TRANSACTIONS' table using Time Travel, specifying the 'AT' or 'BEFORE clause with either the timestamp or query ID of the desired state. Then, replace the original table with the cloned table.
D) Create a new table with the correct data and load from the original table filtered by a range of transaction IDs excluding the incorrect range.
E) Restore the entire Snowflake account to a point in time before the incorrect transformation.

Solutions: