DSA-C03 Exam Dumps | You are tasked with deploying a real-time fraud detection model in Snowflake. The model requires very

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Question 95/143

You are tasked with deploying a real-time fraud detection model in Snowflake. The model requires very low latency (under 100ms) to prevent fraudulent transactions. The input data is streamed into a Snowflake table. You are considering using either a Scalar or Vectorized Python UDF for scoring. Which of the following approaches and considerations are MOST critical for achieving the desired performance and reliability? Assume the model itself is computationally inexpensive. Select all that apply.

A. Use a Scalar UDF because it has lower overhead per invocation compared to a Vectorized UDF when processing individual transactions.

B. Use a Vectorized UDF with a small 'MAX BATCH_SIZE to minimize latency while still leveraging vectorization benefits.

C. Pre-load the model into a static variable within the UDF code, ensuring it's only loaded once per worker node.

D. Utilize Snowflake's Materialized Views to pre-compute frequently used features, reducing the amount of data the UDF needs to process.

E. Configure Snowflake's Auto-Suspend feature to aggressively suspend the warehouse when idle, to minimize costs.

Correct Answer: B,C,D

For real-time fraud detection with low latency requirements, careful optimization is crucial. Vectorized UDFs (B) can be faster than scalar UDFs even with small batch sizes because of the reduced overhead per record compared to scalar UDFs. Pre-loading the model (C) is essential to avoid repeated model loading overhead. Using Materialized Views (D) to pre-compute features reduces the data the UDF needs to handle, improving performance. While scalar UDFs can have lower overhead per invocation, vectorized UDFs optimized with proper will generally provide better performance. Aggressively auto-suspending the warehouse (E) is counterproductive as it introduces latency due to warehouse startup time.

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