DSA-C03 Exam Dumps | You're developing a fraud detection system in Snowflake. You're using Snowflake Cortex to generate embeddings

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Question 83/143

You're developing a fraud detection system in Snowflake. You're using Snowflake Cortex to generate embeddings from transaction descriptions, aiming to cluster similar fraudulent transactions. Which of the following approaches are MOST effective for optimizing the performance and cost of generating embeddings for a large dataset of millions of transaction descriptions using Snowflake Cortex, especially considering the potential cost implications of generating embeddings at scale? Select two options.

A. Generate embeddings on the entire dataset every day to capture all potential fraudulent transactions and ensure the model is always up-to-date.

B. Use a Snowflake Task to incrementally generate embeddings only for new transactions that have been added since the last embedding generation run.

C. Create a materialized view containing pre-computed embeddings for all transaction descriptions.

D. Generate embeddings using snowflake-cortex-embed-text function, using the OPENAI embedding model

E. Implement caching mechanism based on a hash of transaction description if transaction description does not change then no need to recompute the emebeddings again.

Correct Answer: B,E

Option B is a better approach compared to option A to generate embeddings because its incrementally generate embeddings for new transactions. Option E is also an important approach where if transaction description remains same for the embeddings will not be re-computed. Materialized view is not suited for API integrations like those using Snowflake Cortex. Option D is technically correct, but doesn't address the optimization and cost concerns. Option A Regenerating embeddings for the entire dataset daily is computationally expensive and can quickly lead to high costs, especially with Snowflake Cortex. The best approach is to use caching and compute only for a new transaction description. So correct answer is B and E.

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