Consider a table 'EVENT DATA' that stores events from various applications. The table has columns like 'EVENT ID, 'EVENT TIMESTAMP, 'APPLICATION ID', 'USER ID', and 'EVENT _ TYPE. A significant portion of queries filter on 'EVENT TIMESTAMP ranges AND 'APPLICATION ID. The data volume is substantial, and query performance is crucial. You observe high clustering depth after initial loading. Which combination of actions will provide the MOST effective performance optimization, addressing both clustering depth and query performance?

• A. Create multiple materialized views: one filtering on common 'EVENT TIMESTAMP' ranges, and another filtering on common 'APPLICATION ID' values.
• B. Cluster the table on '(EVENT TIMESTAMP, APPLICATION IDY and periodically run 'OPTIMIZE TABLE EVENT DATA' using a warehouse sized appropriately for the table size. Then, monitor clustering depth regularly.
• C. Cluster the table on 'USER ICY and rely solely on Snowflake's automatic reclustering feature, without running 'OPTIMIZE TABLES manually.
• D. Create separate tables for each ' , each clustered on 'EVENT_TIMESTAMP'. Then, create a view that UNION ALLs these tables.
• E. Cluster the table on 'EVENT TIMESTAMP' and periodically run 'OPTIMIZE TABLE EVENT DATA' using a small warehouse. Also, create a separate table clustered on 'APPLICATION

A data engineering team is using Snowflake's data lineage features, and they need to audit changes to data masking policies applied to a table named 'EMPLOYEES'. They want to identify when a masking policy was added, modified, or removed from specific columns.
What are the recommended Snowflake features or audit logs that the data engineering team could use to get these requirements?

• A. The 'INFORMATION SCHEMA.POLICY REFERENCES view to determine what masking policies are currently in place. Then, combine that with the use of Snowflake's Alerting framework to get notified on the creation/removal of tables, and also on changes on the masking policies via SYSTEM$GET_PRIVILEGES() function.
• B. The 'OBJECT DEPENDENCIES' view in the ACCOUNT USAGE schema will directly track changes related to masking policies applied to tables since that is the best place for lineage information.
• C. Snowflake event tables provide complete audit trail capabilities. These tables capture all the events including policies.
• D. Snowflake's native Data Lineage feature automatically captures all changes to data masking policies without any additional configuration, and those changes are then available to the data steward through the user interface.
• E. The Account Usage view 'POLICY REFERENCES coupled with 'QUERY HISTORY, filtering for 'ALTER TABLE MODIFY COLUMN SET MASKING POLICY statements and also comparing snapshots of the 'POLICY_REFERENCES' view over time.

You are tasked with creating a resilient data pipeline using Snowpark Python. The pipeline transforms data from a raw stage to a processed stage. A key transformation involves joining two DataFrames, 'dfl' and 'df2 , based on a common column, 'id'. You want to ensure that even if 'df2 is temporarily unavailable or contains unexpected data, the pipeline continues to process 'dfl' using a default value for missing data from 'df2. Which of the following approaches provides the best balance of resilience and data integrity? Assume you have defined a default dataframe 'df default' already.

• A. Write a custom Python UDF that attempts to retrieve the corresponding data from 'df2 based on the 'id' column. If the retrieval fails for a particular ID, return a default value.
• B. Use 'broadcast hint on 'df2 before performing the join to reduce the chances of join failure, assuming 'df2 is a small dataframe.
• C. Perform a 'left_outer' join of 'dfl' with 'df2. If 'df2 is unavailable or returns no data, replace 'df2' with a default DataFrame Cdf_default) and proceed with the join.
• D. Perform a 'left_outer' join of 'dfl' with 'df2. If the join fails, catch the exception and proceed without the join.
• E. Use a 'try-except' block to catch any exceptions during the join operation. If an exception occurs, use the 'fillna()' method to replace missing values with the default data value.

A data engineer wants to use Snowpark to read a large CSV file from an external stage and infer the schema automatically. However, some columns in the CSV contain data that Snowflake cannot automatically infer the type for. Which of the following code snippets demonstrates the CORRECT way to read the CSV file with schema inference and handle potentially problematic columns by explicitly specifying their data types?

• A.
  
• B.
  
• C.
  
• D.
  
• E.