DSA-C03 Exam Dumps | You have deployed a vectorized Python UDF in Snowflake to perform sentiment analysis on customer reviews.

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Question 143/143

You have deployed a vectorized Python UDF in Snowflake to perform sentiment analysis on customer reviews. The UDF uses a pre-trained transformer model loaded from a Stage. The model consumes a significant amount of memory (e.g., 5GB). Users are reporting intermittent 'Out of Memory' errors when calling the UDF, especially during peak usage. Which of the following strategies, used IN COMBINATION, would MOST effectively mitigate these errors and optimize resource utilization?

A. Increase the value of 'MAX BATCH_ROWS' for the UDF to process larger batches of data at once.

B. Reduce the value of 'MAX for the UDF to process smaller batches of data.

C. Implement lazy loading of the model within the UDF, ensuring it's only loaded once per warehouse node and reused across multiple invocations within that node.

D. Partition the input data into smaller chunks using SQL queries and call the UDF on each partition separately.

E. Increase the warehouse size to provide more memory per node.

Correct Answer: C,D,E

Out of Memory errors in UDFs using large models are common. Increasing the warehouse size (E) provides more memory per node and is a direct solution. Lazy loading (C) ensures the model is loaded only once per node, reducing overall memory footprint. Partitioning the data (D) into smaller chunks prevents overwhelming a single UDF invocation with too much data. Decreasing (B) is generally a helpful strategy as well. Increasing WAX BATCH ROWS' (A) would likely exacerbate the memory issues by increasing the batch size and processing even more data at once.

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