A team is developing a machine learning model by training on the latest Snowflake features. The training is taking much longer than expected to complete.
Which step will accelerate the model training?
A.
Increase the size of the virtual warehouse.
B.
Add additional clusters to the virtual warehouse.
Snowpark‑optimized warehouses are specifically designed to accelerate memory‑intensive workloads—such as single‑node ML training—by providing up to 16× the memory and 10× the local cache per node compared to standard warehouses. They let you run your Snowpark Python stored procedures (including model training) directly in Snowflake without changing your code, and they inherit all the elasticity and security of regular virtual warehouses. Neither adding clusters (which targets concurrency, not per‑job performance) nor the Query Acceleration Service (which optimizes analytic queries) will improve the in‑warehouse training speed. Likewise, simply increasing warehouse size may not provide the specialized memory and caching benefits that Snowpark‑optimized warehouses deliver.
Answer: C. Use a Snowpark‑optimized virtual warehouse.
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Billhardy
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