A data engineer is working with a Snowpark DataFrame 'sales df containing sales data with columns 'product id', 'sale_date', and 'sale amount'. The engineer needs to calculate the cumulative sales amount for each product over time. Which of the following code snippets using window functions correctly calculates the cumulative sales amount, ordered by 'sale date'?

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

You are building a data pipeline that extracts data from a REST API, transforms it using Pandas DataFrames, and loads it into Snowflake. You need to implement error handling to gracefully handle network issues and API rate limits. Which of the following code snippets demonstrates the most robust approach to handle potential errors during data loading into Snowflake using the Python connector?

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

You are troubleshooting a slowly performing query in Snowflake that aggregates data from a large ORDERS table (10 billion rows) partitioned by ORDER DATE. The query execution plan shows significant 'Remote Spill to Disk'. Which of the following actions would be MOST effective in reducing the spill and improving query performance? Assume all statistics are up-to-date and the data is properly clustered by ORDER_DATE.

• A. Increase the value of the parameter. This allows the warehouse to scale up further if needed.
• B. Optimize the query to leverage data pruning based on ORDER DATE by ensuring the query filters on a specific or limited range of ORDER DATE values.
• C. Increase the virtual warehouse size. This will provide more memory for the query to execute.
• D. Reduce the number of columns selected in the query, only selecting those that are essential for the aggregation.
• E. Rewrite the query to use window functions instead of aggregate functions.

A data engineering team is implementing a data governance strategy in Snowflake. They need to track the lineage of a critical table 'SALES DATA' from source system ingestion to its final consumption in a dashboard. They have implemented masking policies on sensitive columns in 'SALES DATA. Which combination of Snowflake features and actions will MOST effectively allow them to monitor data lineage and object dependencies, including visibility into masking policies?

• A. Create a custom metadata repository and use Snowflake Scripting to parse query history and object metadata periodically. Manually track dependencies and policy changes by analyzing the output.
• B. Use the INFORMATION_SCHEMA views like 'TABLES', 'COLUMNS', and 'POLICY_REFERENCES'. These views, combined with custom queries to analyze query history logs, will provide a complete lineage and masking policy overview.
• C. Enable Account Usage views like 'QUERY_HISTORY, and 'ACCESS_HISTORY. These views directly show table dependencies and policy applications.
• D. Utilize Snowflake's Data Governance features, specifically enabling Data Lineage using Snowflake Horizon and utilize the view along with query the 'QUERY HISTORY view. These features natively track data flow and policy application.
• E. Rely solely on a third-party data catalog tool that integrates with Snowflake's metadata API. These tools automatically track lineage and policy information and provide the best and most effective results.

You are developing a data pipeline that extracts data from an on-premise PostgreSQL database, transforms it, and loads it into Snowflake. You want to use the Snowflake Python connector in conjunction with a secure method for accessing the PostgreSQL database. Which of the following approaches provides the MOST secure and manageable way to handle the PostgreSQL connection credentials in your Python script when deploying to a production environment?

• A. Hardcode the PostgreSQL username and password directly into the Python script.
• B. Store the PostgreSQL username and password in environment variables and retrieve them in the Python script using 'os.environ'
• C. Store the PostgreSQL username and password in a configuration file (e.g., JSON or YAML) and load the file in the Python script.
• D. Prompt the user for the PostgreSQL username and password each time the script is executed.
• E. Store the PostgreSQL username and password in a dedicated secrets management service (e.g., AWS Secrets Manager, HashiCorp Vault, Azure Key Vault) and retrieve them in the Python script using the appropriate API.