You are tasked with building a robust data quality monitoring system for a Snowflake data pipeline. The pipeline processes customer order data and loads it into a 'CUSTOMER ORDERS table. You need to implement checks to ensure that certain critical columns (e.g., 'ORDER ID, 'CUSTOMER ID', 'ORDER DATE, meet specific data quality requirements (e.g., not null, valid format, within acceptable range). You want to design a flexible and scalable solution that allows you to easily add, modify, and monitor data quality rules. Select the options to implement that and scale efficiently Assume there is a central Data Quality table for each metrics

• A. Build a set of custom Snowflake Native Apps to monitor and report on data quality. Each App will focus on one or more critical tables or data quality checks
• B. Develop a parameterized stored procedure that accepts the table name, column name, data quality rule definition, and threshold values as input parameters. This procedure then dynamically constructs and executes the SQL query to check the data quality rule.
• C. Utilize Snowflake's native Data Governance features, such as data masking and row-level security, to enforce data quality rules.
• D. Create a series of individual SQL scripts, each checking a specific data quality rule for a specific column, and schedule these scripts to run using Snowflake tasks.
• E. Implement a Snowpark Python UDF that leverages a data quality library (e.g., Great Expectations) to define and execute data quality rules. The UDF takes a DataFrame representing the data to be checked and returns a DataFrame containing the data quality check results.

You are tasked with calculating the daily moving average of sales for each product category in your Snowflake data warehouse using Snowpark Python. You need to handle cases where there might be missing sales data for certain days. You have the following sales data available in a table named with columns: (DATE), (VARCHAR), and 'sales_amount' (NUMBER). Which of the following Snowpark Python code snippets correctly calculates the 7-day moving average of sales amount per product category, filling in missing sales days with 0, and handles potential division by zero?

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

You are designing a data governance strategy for a Snowflake data warehouse. You need to track data lineage for compliance purposes. Specifically, you need to identify all downstream tables that depend on a specific column in a source table. Which combination of Snowflake features and techniques would you use to achieve this goal effectively?

• A. Use Snowflake's ACCOUNT USAGE views related to query history and object dependencies, combined with a custom script to recursively trace data lineage based on SQL operations (e.g., INSERT INTO ... SELECT).
• B. Rely solely on user documentation and training to ensure data lineage is properly documented and maintained. Implement strict naming conventions for tables and columns.
• C. Use Snowflake's INFORMATION SCHEMA views (TABLES, COLUMNS) and regularly audit user query history to manually reconstruct the data lineage.
• D. Implement a custom data lineage tracking system by parsing all SQL queries executed in the Snowflake environment and storing the dependencies in a separate metadata database.
• E. Utilize Snowflake's data lineage feature in conjunction with object tagging. Tag relevant columns and tables, then query the lineage views to trace dependencies.

You are responsible for monitoring the performance of a Snowflake data pipeline that loads data from S3 into a Snowflake table named 'SALES DATA. You notice that the COPY INTO command consistently takes longer than expected. You want to implement telemetry to proactively identify the root cause of the performance degradation. Which of the following methods, used together, provide the MOST comprehensive telemetry data for troubleshooting the COPY INTO performance?

• A. Query the 'COPY HISTORY view in the 'INFORMATION SCHEMA' and enable Snowflake's query profiling for the COPY INTO statement.
• B. Query the 'COPY HISTORY view in the 'INFORMATION SCHEMA' and monitor CPU utilization of the virtual warehouse using the Snowflake web I-Jl.
• C. Use Snowflake's partner connect integrations to monitor the virtual warehouse resource consumption and query the 'VALIDATE function to ensure data quality before loading.
• D. Query the ' LOAD_HISTORY function and monitor the network latency between S3 and Snowflake using an external monitoring tool.
• E. Query the 'COPY_HISTORY view and the view in 'ACCOUNT_USAG Also, check the S3 bucket for throttling errors.

You accidentally truncated a large table named 'SALES DATA' in your 'REPORTING DB" database. You realize this happened 2 days ago, and your account has the default Time Travel retention of 1 day. You need to recover this table with minimal downtime. Analyze the situation and determine the best course of action, considering cost and recovery time.

• A. Increase the account-level to 2 days and then use the UNDROP TABLE SALES_DATA' command.
• B. Raise a support ticket requesting data recovery from failsafe. Since data retention period has expired.
• C. Immediately contact Snowflake Support to initiate a restore from Fail-safe, understanding that this process may take several hours or even days.
• D. Because the data retention period has expired, the table is unrecoverable using Snowflake's built-in features; you must restore from an external backup solution if available.
• E. Create a clone of the table using the 'AT clause and a timestamp from 1 day ago. This would prevent any additional cost.