A data engineer observes that a Snowflake query, used for generating a daily sales report, consistently runs slower each day, despite the dataset size remaining relatively stable. The query joins a large sales table (SALES) with a smaller product dimension table (PRODUCT) on PRODUCT ID. You've already confirmed that virtual warehouse sizing is adequate and data clustering is enabled on SALES(SALE DATE). Analyze the following scenarios and identify the MOST likely cause of the performance degradation and potential solution:

• A. The Snowflake query optimizer is selecting a suboptimal execution plan because the query is complex. Rewrite the query using temporary tables to break down the logic into smaller steps.
• B. The data clustering on SALES(SALE DATE) is ineffective as the query also filters on PRODUCT CATEGORY, which isn't clustered. Re-cluster SALES by both SALE DATE and PRODUCT CATEGORY.
• C. The statistics on the PRODUCT table are outdated. Run 'ANALYZE TABLE PRODUCT' to refresh them.
• D. The join between SALES and PRODUCT is causing excessive data spill to local storage due to an inefficient join order. Consider using a JOIN hint, specifically a BROADCAST hint on the PRODUCT table (small table).
• E. The virtual warehouse is being overwhelmed by concurrent queries. Implement workload management rules to prioritize the sales report query.

You are tasked with optimizing a data pipeline that loads data from an external cloud storage location into Snowflake, transforms it, and then loads it into reporting tables. The pipeline is experiencing intermittent performance issues. You want to proactively identify and address these issues. Which of the following monitoring techniques and Snowflake features would be MOST effective for continuous monitoring and performance optimization?

• A. Implement custom logging and monitoring using Snowflake Scripting and User-Defined Functions (UDFs) to capture granular performance metrics at each stage of the pipeline and push notifications via external functions to a monitoring service.
• B. Utilize Snowflake's System Functions to periodically query performance views (e.g., 'QUERY_HISTORY, ' and write aggregated metrics to a dedicated monitoring table. Configure a scheduled task to generate alerts based on predefined thresholds.
• C. Rely solely on Snowflake's default query history and resource monitors. These automatically track performance and usage, providing sufficient insight without additional configuration.
• D. Focus exclusively on optimizing SQL queries and data transformations. Monitoring is unnecessary since Snowflake automatically handles performance optimization.
• E. Enable Snowflake's Auto-Suspend and Auto-Resume features on the warehouse. This is the most efficient way to manage resources and optimize costs, indirectly addressing performance concerns.

You have a table named 'TRANSACTIONS which is frequently queried by 'TRANSACTION_DATE and 'CUSTOMER ID. You want to define a clustering strategy for this table. You are aware that defining multiple clustering keys is possible. Given the following considerations, which of the following clustering strategies would provide the BEST performance AND minimize reclustering costs, assuming both columns have similar cardinality and are equally used in WHERE clauses? (Assume cost optimization is the most critical factor if performance difference is minimal.)

• A.
  
• B.
  
• C.
  
• D.
  
• E. Create two separate tables: one clustered by 'TRANSACTION DATE and another clustered by 'CUSTOMER ID', and use appropriate views to redirect queries to the correct table.

You are responsible for monitoring data quality in a Snowflake data warehouse. Your team has identified a critical table, 'CUSTOMER DATA, where the 'EMAIL' column is frequently missing or contains invalid entries. You need to implement a solution that automatically detects and flags these anomalies. Which of the following approaches, or combination of approaches, would be MOST effective in proactively monitoring the data quality of the 'EMAIL' column?

• A. Utilize an external data quality tool (e.g., Great Expectations, Deequ) to define and run data quality checks on the 'CUSTOMER DATA' table, integrating the results back into Snowflake for reporting and alerting.
• B. Use Snowflake's Data Quality features (if available) to define data quality rules for the 'EMAILS column, specifying acceptable formats and thresholds for missing values. Configure alerts to be triggered when these rules are violated.
• C. Schedule a daily full refresh of the 'CUSTOMER DATA' table from the source system, overwriting any potentially corrupted data.
• D. Create a Snowflake Task that executes a SQL query to count NULL 'EMAIL' values and invalid 'EMAIL' formats (using regular expressions). The task logs the results to a separate monitoring table and alerts the team if the count exceeds a predefined threshold.
• E. Implement a Streamlit application connected to Snowflake that visualizes the percentage of NULL and invalid 'EMAIL' values over time, allowing the team to manually monitor trends.

You are tasked with creating a JavaScript UDF in Snowflake to parse JSON data containing nested arrays of objects. The UDF needs to extract specific values from these nested objects and return them as a comma-separated string. Given the JSON structure below, and the requirement to extract the 'value' field from each object within the 'items' array located inside each element of the 'data' array, which of the following JavaScript UDF definitions will correctly achieve this, assuming the input JSON is passed as a string?

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