SOL-C01 Exam Dumps | A Snowflake Notebook is being used to train a machine learning model. The notebook contains multiple

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Snowflake.SOL-C01.v2026-01-13.q155
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Question 107/155

A Snowflake Notebook is being used to train a machine learning model. The notebook contains multiple cells: one to load data, one to perform feature engineering, and one to train the model using Snowpark ML. After running the feature engineering cell, the notebook session unexpectedly terminates. No error messages are displayed, and the notebook kernel restarts automatically. The data being processed is approximately 50 GB. What are the TWO most likely causes of this issue and their corresponding remedies?

A. The notebook session timed out due to inactivity. Increase the SESSION_IDLE_TIMEOUT setting for the Snowflake user or account.

B. The Snowflake warehouse being used for the notebook session is undersized, leading to memory exhaustion and session termination. Scale up the warehouse to a larger size (e.g., from X-Small to Small or Medium).

C. The Snowpark ML library encountered an incompatibility issue with the current Snowflake version.
Upgrade or downgrade the Snowpark ML version to match the compatible Snowflake version.

D. The feature engineering process resulted in an excessively large intermediate dataset that exceeded available memory. Optimize the feature engineering code to reduce memory consumption or partition the data.

E. The notebook is configured to automatically save checkpoints. This feature is causing overhead and instability when dealing with large datasets. Disable auto- saving in the notebook settings.

Correct Answer: B,D

Warehouse size (B) is crucial for processing large datasets. An undersized warehouse will quickly run out of memory, leading to session termination. Memory exhaustion (D) from feature engineering is also a common cause. Optimizing the code and partitioning data can help. While session timeouts (A) are possible, the immediate termination after running the cell suggests a memory issue. Snowpark ML incompatibility (C) is less likely if the basic loading and processing work. Auto-saving (E) contributes to overhead but is unlikely to be the primary cause of immediate termination.

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