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

1. You are loading JSON data into a Snowflake table with a 'VARIANT' column. The JSON data contains nested arrays with varying depths. You need to extract specific values from the nested arrays and load them into separate columns in your Snowflake table. Which approach would provide the BEST performance and flexibility?

A) Create a view with nested 'FLATTEN' functions to extract the values from the 'VARIANT column. The view serves as the source for further transformations.
B) Load the entire JSON into a 'VARIANT column and then use SQL with nested 'FLATTEN' functions to extract the desired values during query time.
C) Use Snowpipe with auto-ingest, loading directly into the table with the 'VARIANT column. Define data quality checks with pre-load data transformation.
D) Use a 'COPY' command with a 'TRANSFORM' clause that uses JavaScript UDFs to parse the JSON and extract the values during the load process. Load the extracted values directly into the target columns.
E) Use a stored procedure to parse the JSON data and insert values into the table row by row.

2. A critical database, 'PRODUCTION DB', in your Snowflake account was accidentally dropped. You need to restore it as quickly as possible, but you're unsure if Time Travel retention is sufficient. Which method guarantees restoration of the database even if it falls outside the Time Travel window?

A) Fail-safe cannot be directly accessed by the user for restoration purposes; it is only used by Snowflake Support in extreme disaster recovery scenarios.
B) Restore from a Snowflake-managed backup using the 'CREATE DATABASE ... FROM BACKUP' command. Specify the timestamp before the drop occurred.
C) Contact Snowflake Support and request restoration from Fail-safe.
D) Use the 'UNDROP DATABASE PRODUCTION command.
E) Utilize the data cloning feature: 'CREATE DATABASE CLONE PRODUCTION_DB BEFORE (STATEMENT 'DROP DATABASE PRODUCTION_DB');'

3. A financial services company is using Snowflake Streams on a table 'TRANSACTIONS' to capture changes for auditing purposes. The 'TRANSACTIONS' table contains sensitive data, and the auditing team requires the stream to only capture changes to specific columns: 'ACCOUNT ID', 'TRANSACTION DATE', and 'TRANSACTION AMOUNT'. Which of the following approaches is the MOST efficient and secure way to achieve this requirement, ensuring minimal performance impact and data exposure?

A) Create a View that selects only the 'ACCOUNT ID, 'TRANSACTION DATE, and 'TRANSACTION AMOUNT columns and create a Stream on the View.
B) Create a Stream on the 'TRANSACTIONS' table and use a masking policy on the stream's output to redact the unnecessary columns.
C) Create a standard Stream on the 'TRANSACTIONS table and then filter the results in downstream processing to only include the required columns.
D) Create a Stream on the 'TRANSACTIONS' table. Periodically truncate stream and reload all data from TRANSACTION table by applying filter while loading.
E) Create a task that clones the TRANSACTIONS table and a stream on that cloned table, limiting what changes are captured using a WHERE clause on the cloning command.

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 tasked with implementing a data recovery strategy for a critical table 'SALES DATA' in Snowflake. The table is frequently updated, and you need to ensure you can recover to a specific point in time in case of accidental data corruption. Which approach provides the most efficient and granular recovery option, minimizing downtime and data loss? Consider performance and storage implications of each method.

A) Creating a Snowflake Stream on 'SALES_DATR and capturing all DML changes for point-in-time recovery.
B) Using the 'UNDROP TABLE command in conjunction with the 'AT' clause to revert the table to a previous state.
C) Regularly creating full clones of the 'SALES_DATR table to a separate database.
D) Create a scheduled task that takes a snapshot of the sales data and store it to an external staging location.
E) Relying solely on Snowflake's Time Travel feature with the default data retention period.

Solutions: