You have configured a Kafka Connector to load JSON data into a Snowflake table named 'ORDERS. The JSON data contains nested structures. However, Snowflake is only receiving the top- level fields, and the nested fields are being ignored. Which configuration option within the Kafka Connector needs to be adjusted to correctly flatten and load the nested JSON data into Snowflake?

• A. Set the 'value.converter.schemas.enable' property to 'true'.
• B. Apply the 'org.apache.kafka.connect.transforms.Flatten' transformation to the 'transforms' configuration.
• C. Use the 'transforms' configuration with the 'org.apache.kafka.connect.transforms.ExtractField$Value' transformation to extract specific fields.
• D. Configure the 'snowflake.data.field.name' property to specify the column in the Snowflake table where the entire JSON should be loaded as a VARIANT.
• E. Enable the 'snowflake.ingest.stage' property and set it to a Snowflake internal stage.

You're tasked with building a data pipeline using Snowpark Python to incrementally load data into a target table 'SALES SUMMARY from a source table 'RAW SALES. The pipeline needs to ensure that only new or updated records from 'RAW SALES are merged into 'SALES SUMMARY' based on a 'TRANSACTION ID'. You want to use Snowpark's 'MERGE' operation for this, but you also need to handle potential conflicts and log any rejected records to an error table 'SALES SUMMARY ERRORS'. Which of the following approaches offers the MOST robust and efficient solution for handling errors and ensuring data integrity within the MERGE statement?

• A. Employ the 'MERGE statement with 'WHEN MATCHED THEN UPDATE' and 'WHEN NOT MATCHED THEN INSERT clauses, and use a stored procedure that executes the 'MERGE statement and then conditionally inserts rejected records into the 'SALES SUMMARY ERRORS' table based on criteria defined within the stored procedure. This will use the table function on the output.
• B. Use a single 'MERGE statement with 'WHEN MATCHED THEN UPDATE and 'WHEN NOT MATCHED THEN INSERT clauses. Capture rejected records by leveraging the ' SYSTEM$PIPE STATUS function after the 'MERGE operation to identify rows that failed during the merge.
• C. Use the 'WHEN MATCHED THEN UPDATE' clause to update existing records and the 'WHEN NOT MATCHED THEN INSERT clause to insert new records. Implement a separate process to periodically compare 'SALES_SUMMARY with 'RAW_SALES' to identify and log any inconsistencies.
• D. Utilize the 'WHEN MATCHED THEN UPDATE and 'WHEN NOT MATCHED THEN INSERT clauses with a 'WHERE' condition in each clause to filter out potentially problematic records. Log these filtered records to using a separate 'INSERT statement after the 'MERGE operation.
• E. Incorporate an 'ELSE clause in the 'MERGE' statement to capture records that do not satisfy the update or insert conditions due to data quality issues. Use this 'ELSE clause to insert rejected records into 'SALES SUMMARY ERRORS'

You have a table 'EVENTS' containing application event data with columns 'EVENT ID, 'USER ID, 'EVENT TYPE, and EVENT DETAILS (VARCHAR). The 'EVENT DETAILS column contains comma-separated key-value pairs (e.g., 'location=USA,device=mobile,os=iOS'). Your objective is to transform this structured data into a VARIANT column named EVENT JSON' in a new table 'EVENTS JSON'. The data in EVENT DETAILS has inconsistent key-value pairs across different rows. Which of the following methods are the most efficient and scalable to parse the key-value pairs in 'EVENT DETAILS' and construct the JSON objects?

• A. Use a combination of 'SPLIT, 'REGEXP_REPLACE and 'OBJECT_CONSTRUCT within a user-defined function (UDF) to parse the string and build the JSON object.
• B. Use a Java UDF that iterates through the string, splitting it based on commas and equals signs, and then constructs a JSON object using a JSON library.
• C. Utilize to split the key-value pairs into rows, then use 'REGEXP_EXTRACT to extract the key and value. Finally, use 'OBJECT_CONSTRUCT and to construct the JSON object.
• D. Use 'SPLIT to split the key-value pairs into an array, then use a LATERAL FLATTEN to create rows from array, then use 'SPLIT again to split each row by '='. Finally, construct the JSON using 'OBJECT CONSTRUCT.
• E. Use only REGEXP EXTRACT ALL' with appropriate regular expressions to extract all keys and values into arrays, then use a JavaScript UDF to combine them into a JSON object.

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

• A. Create a view that casts the 'EVENT DATA' column to VARCHAR before extracting attributes.
• B. Create a new table with columns for 'item_id' and 'price' using the 'EVENT DATA column. Refreshed in a regular interval and used in Downstream querying.
• C. Create a virtual column for 'item_id' and 'price' using JSON path expressions and create indexes on these virtual columns.
• D. Create a search optimization service for the table 'USER_ACTIVITY to help filtering data in downstream
• E. Use the ' GET_PATH' function repeatedly to extract 'item_id' and 'price' in the main query.