DSA-C03 Exam Dumps | You've trained a sales forecasting model using Snowpark ML and want to deploy it within Snowflake for

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Question 49/143

You've trained a sales forecasting model using Snowpark ML and want to deploy it within Snowflake for real-time predictions. You've decided to store the predictions directly in a Snowflake table. The model predicts sales for different product categories based on historical data and promotional activities. Which of the following approaches is the MOST efficient and scalable way to store these predictions, considering a high volume of prediction requests and the need for quick retrieval for downstream dashboards?

A. Storing predictions in a single, wide table with all features and predictions as columns. No partitioning or clustering is implemented.

B. Storing predictions in a separate table with a composite key of product category and timestamp, with clustering on the timestamp column and partitioning by product category.

C. Storing predictions in a VARIANT column in a single table. All prediction results for a given product category are stored as a JSON document within the VARIANT column.

D. Storing predictions in an external stage (e.g., AWS S3) and querying them using an external table. The external table definition includes the sales prediction as a column.

E. Storing predictions in a key-value store like Redis and referencing the keys from a Snowflake table. Requires external network access from Snowflake.

Question 49/143

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