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

1. You are configuring cross-cloud replication for a Snowflake database named 'SALES DB' from an AWS (us-east-I) account to an Azure (eastus) account. You have already set up the necessary network policies and security integrations. However, replication is failing with the following error: 'Replication of database SALES DB failed due to insufficient privileges on object 'SALES DB.PUBLIC.ORDERS'.' What is the MOST LIKELY cause of this issue, and how would you resolve it? (Assume the replication group and target database exist).

A) The target Azure account does not have sufficient storage capacity. Increase the storage quota for the Azure account.
B) The replication group does not have the necessary permissions to access the 'ORDERS' table in the AWS account. Grant the 'OWNERSHIP' privilege on the 'ORDERS table to the replication group: 'GRANT OWNERSHIP ON TABLE SALES DB.PUBLIC.ORDERS TO REPLICATION GROUP
C) The network policy is blocking access to the ORDERS table. Update the network policy to allow access to the ORDERS table.
D) The user account performing the replication does not have the 'ACCOUNTADMIN' role in the AWS account. Grant the 'ACCOUNTADMIN' role to the user.
E) The replication group is missing the 'ORDERS' table. Alter the replication group to include the 'ORDERS' table: 'ALTER REPLICATION GROUP ADD DATABASE SALES DB;'

2. A data engineer accidentally truncated a critical table 'ORDERS' in the 'SALES DB' database. The table contained important historical order data, and the data retention period is set to the default. Which of the following options represents the MOST efficient and reliable way to recover the truncated table and its data, minimizing downtime and potential data loss?

A) Use Time Travel to create a clone of the truncated table from a point in time before the truncation. Then, swap the original table with the cloned table.
B) Create a new table 'ORDERS' and manually re-insert the data from the application's logs and backups.
C) Contact Snowflake support and request them to restore the table from a system-level backup.
D) Restore the entire Snowflake account to a previous point in time before the table was truncated.
E) Use the UNDROP TABLE command to restore the table. If UNDROP fails, clone the entire SALES_DB database to a point in time before the truncation using Time Travel.

3. You have a Python UDF in Snowflake designed to enrich customer data by calling an external API to retrieve additional information based on the customer ID. Due to API rate limits, you need to implement a mechanism to cache API responses within the UDF to avoid exceeding the limits. The UDF is defined as follows:

Which caching mechanism can be implemented MOST effectively WITHIN the Python UDF to minimize API calls while adhering to Snowflake's UDF limitations?

A) Persist the API responses in a temporary table within Snowflake. The UDF will first query the temporary table for the customer ID; if found, return the cached data. Otherwise, call the API and store the response in the temporary table for future use.
B) Utilize Snowflake's built-in caching mechanisms (result caching) by ensuring the UDF is deterministic and only depends on its input parameters. Snowflake will automatically cache the results of the UDF for subsequent calls with the same input.
C) Create a global dictionary within the UDF to store the API responses, using the customer ID as the key. Before calling the API, check if the customer ID exists in the dictionary; if it does, return the cached response. This approach will keep cached values during the session.
D) Use the 'functools.lru_cache' decorator to cache the results of the 'get_customer details' function within the UDF's scope. This will automatically cache the most recently used API responses.
E) Leverage external caching services like Redis by making API calls to Redis from the UDF to store and retrieve cached API responses. This would require configuring Snowflake to connect with external systems.

4. You are designing a CI/CD pipeline for your Snowflake data transformations. One stage involves testing a new stored procedure that modifies several tables in your data warehouse. To ensure data integrity and proper rollback capabilities during testing in your development environment, you want to use a combination of cloning and Tme Travel. Select the option that represents the most robust strategy for testing with the ability to revert to the original state in case of failures. Choose all that apply.

A) Create a new database by cloning the existing database before the transformations. Use the cloned database for all development and testing.
B) Before executing the stored procedure in development, create clones of all affected tables and store the clone names in a configuration file for rollback if needed.
C) Immediately after executing the stored procedure, execute 'UNDROP TABLE' on all the affected tables. If there were errors, it would restore data from the point of the procedure execution.
D) Leverage Snowflake's Time Travel feature to create a named snapshot of each affected table immediately before running the stored procedure. Use the 'AT(TIMESTAMP ...)' or 'BEFORE(STATEMENT => ...)' syntax within your testing framework, for easy reversion.
E) Create a clone of the schema containing all the tables affected by the stored procedure before executing the procedure in the development environment. This will create point in time backup to rollback at schema level.

5. A financial institution needs to mask sensitive customer data (PII) in a 'CUSTOMER' table. The table contains columns like 'CUSTOMER ID', 'FIRST NAME', 'LAST NAME', 'CREDIT CARD, and 'ADDRESS'. The data should be masked differently for different roles: 'ANALYST' role should see obfuscated values for names and addresses, while the 'SUPPORT' role should see the last four digits of the credit card and a hashed version of the address. The "CUSTOMER ID' should never be masked. Assume a central masking policy already exists called 'PII MASKING POLICY. Which of the following statements is the MOST efficient and secure way to achieve this?

A) Create a single masking policy with a complex stored procedure that checks the current role and applies different masking functions accordingly, then apply this policy to all sensitive columns.
B) Create view for each role which applies masking functions to the columns. Grant SELECT access on those views to relevant roles.
C) Create external functions to handle the complex masking logic and call them from the masking policy.
D) Create multiple masking policies, one for each role and sensitive column combination, each with the appropriate masking expression. Then, apply each masking policy individually to its respective column. Use the function to implement role-based masking within each policy.
E) Create multiple masking policies with different masking expressions and apply them directly to the columns based on the role using conditional expressions within the policies. Use 'CASE statements within the masking policy to differentiate between roles.

Solutions: