A data analyst is working with JSON data representing product reviews. The JSON structure is complex, containing nested arrays and objects. The analyst needs to extract all the reviewer names (reviewer _ name) who gave a rating greater than 4, along with the product ID (product_id) and the overall average rating of that specific product. The table 'RAW REVIEWS contains a single VARIANT column named holding the JSON data'. Choose the most efficient and correct Snowflake SQL query to achieve this. Assume the JSON structure is consistent across all rows.

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

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What actions are involved in performing general DML (Data Manipulation Language) operations in Snowflake? (Select all that apply)

• A.Merging data from multiple tables
• B.Updating existing data
• C.Deleting data entirely
• D.Inserting new data

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You are tasked with diagnosing a performance bottleneck in a daily ETL process that loads data into a Snowflake table called 'SALES DATA'. The ETL process has been running slower than usual for the past week. You suspect a change in the source data volume or distribution. Which of the following Snowflake features and SQL queries would be MOST helpful in identifying the root cause?

• A.Use Snowflake's Query Profile feature to analyze the execution plan of the ETL queries and identify the stages consuming the most time.
• B.Query Snowflake's INFORMATION SCHEMA.QUERY HISTORY view to compare the execution times of recent ETL runs with historical averages, filtering by query ID or user.
• C.Analyze the 'SALES DATA' table's clustering keys and statistics using 'SHOW TABLES LIKE 'SALES DATA';' and 'DESCRIBE TABLE SALES DATA;' to determine if the data distribution has changed significantly, potentially leading to inefficient query performance.
• D.Use Snowflake's Time Travel feature to compare the size and structure of the 'SALES DATA table at different points in time, specifically before and after the performance degradation started.
• E.Run 'SELECT COUNT( ) FROM SALES DATA;' to check total record count and compare against historical values.

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What are the PRIMARY reasons for using integrity constraints on Snowflake tables? (Select TWO).

• A.To enforce that a value in a foreign key must match the value of a primary or unique key in a parent table.
• B.To enforce that records with a NOT NULL column must have values in that column.
• C.To enable team members to see how tables relate to one another.
• D.To enforce that records with a UNIQUE column must have all distinct values in that column.
• E.To allow for clustering on the primary and foreign keys by specifying them as clustering keys.

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You are examining sales data for an e-commerce company in Snowflake. You notice a recurring dip in sales every Wednesday. The 'SALES' table contains columns: 'order_id', 'order date' , 'product_id', 'quantity', 'price'. Which of the following SQL queries and analytical techniques would be the MOST effective for diagnosing the reasons behind this weekly sales dip, assuming no external promotional activity always happens on Wednesdays?

• A.Run a query to calculate the average daily sales for each day of the week and visualize the results to confirm the dip on Wednesdays. Use "DAYOFWEEK' function for this.
• B.Analyze sales patterns by 'product_id' and day of the week to see if specific product categories are driving the overall dip on Wednesdays. Use 'PIVOT' operator for cross-tabulation.
• C.Investigate shipping times by day of the week to determine if longer shipping times on orders placed earlier in the week are affecting Wednesday sales. Join ' SALES table with 'SHIPPING' table and calculate difference between 'order_date' and 'ship_date' .
• D.Check if there are any scheduled maintenance windows or system outages occurring on Wednesdays that might be affecting order processing. Use 'INFORMATION SCHEMA.LOAD HISTORY' to look for any loads failing on wednesdays
• E.Review recent code deployments related to the e-commerce platform and identify if any changes were implemented on Tuesdays or Wednesdays that could be impacting sales.

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