You are designing a data product for the Snowflake Marketplace that provides daily weather forecasts. You need to ensure that consumers of your data receive the latest forecast data every morning automatically with minimal latency. Which of the following strategies offers the MOST efficient and cost-effective solution for updating the shared data?

• A. Create a scheduled task that executes a full refresh of the shared tables every morning at 6 AM. This task uses CREATE OR REPLACE TABLE AS SELECT to rebuild the tables with the latest forecast data.
• B. Create a stored procedure that truncates and reloads the shared tables with the latest forecast data from a staging table. Schedule this stored procedure to run every morning at 6 AM using a Snowflake task.
• C. Share the raw data files stored in an external stage with the consumers. Consumers will then need to create their own pipelines to process and load the data.
• D. Implement a continuous data pipeline using Snowflake Streams and Tasks to incrementally update the shared tables as new forecast data becomes available. The stream tracks changes and tasks apply those changes to the shared tables.
• E. Manually upload a new CSV file containing the latest forecast data to a Snowflake stage and then load it into the shared tables every morning at 6 AM.

You are tasked with designing a data pipeline to load data from an Azure Blob Storage container into Snowflake using an external stage. The data is in CSV format, compressed using GZIP. The container contains millions of small CSV files. To optimize the data loading process and minimize cost, which of the following strategies would you implement, considering both stage configuration and COPY INTO options? Choose TWO that apply.

• A. Leverage Snowflake's Snowpipe with a REST API endpoint to trigger data loads whenever new files are available in the Azure Blob Storage container.
• B. Use the 'MATCH BY COLUMN NAME = CASE INSENSITIVE option with a copy transformation in the 'COPY INTO' statement to ensure that the column order in the CSV files doesn't affect the data load.
• C. Consolidate the small CSV files in the Azure Blob Storage container into larger files before loading them into Snowflake. This reduces the overhead of processing numerous small files.
• D. Create a pipe object with 'AUTO INGEST = TRUE to automatically ingest new files as they are added to the Azure Blob Storage container. This ensures near real-time data ingestion.
• E. Use the 'VALIDATION MODE = RETURN ERRORS option in the 'COPY INTO' statement to identify and correct any data quality issues during the load. This ensures that only clean data is loaded into Snowflake.

Consider a scenario where you have a Snowflake external table 'ext_logs' pointing to log files in an S3 bucket. The log files are continuously being updated, and new files are added frequently. You want to ensure that your external table always reflects the latest data available in S3. Which of the following actions and configurations are required or recommended to keep the external table synchronized with the underlying data source? (Select all that apply)

• A. Configure an event notification service (e.g., AWS SQS) to trigger an external table refresh whenever new files are added to S3.
• B. Enable auto-refresh on the external table using the 'AUTO_REFRESH = TRUE parameter during creation.
• C. Implement a stored procedure that periodically executes a query on the external table to force a metadata refresh.
• D. Periodically execute the 'ALTER EXTERNAL TABLE ext_logs REFRESH' command to update the metadata about the files in S3.
• E. Create a Snowpipe that continuously loads data from the S3 bucket into a Snowflake table instead of using an external table.

You're designing a data pipeline in Snowflake that utilizes an external function to perform sentiment analysis on customer reviews using a third-party NLP service. This service charges per request. You need to minimize costs while ensuring timely processing of the reviews.
Which of the following strategies would be most effective in optimizing the cost and performance of your external function?

• A. Bypass the external function completely and rely solely on Snowflake's built-in NLP functions for sentiment analysis.
• B. Pre-process the customer reviews in Snowflake to filter out irrelevant reviews (e.g., very short reviews or reviews with stop words) before sending them to the external function.
• C. Implement a caching mechanism (e.g., using a Snowflake table or an external cache) to store the sentiment analysis results for frequently occurring reviews or similar text patterns, avoiding redundant API calls.
• D. Set 'MAX BATCH_ROWS' to a very high value (e.g., 10000) to maximize the number of rows processed per API call, even if it increases latency for individual reviews.
• E. Implement rate limiting and error handling in the external service (e.g., AWS Lambda or Azure Function) to gracefully handle API usage limits and prevent excessive charges due to errors.