SOL-C01 Question #291: Real Exam Question with Answer & Explanation

Question

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?

Options

• AThe notebook session timed out due to inactivity. Increase the SESSION_IDLE_TIMEOUT setting
• BThe Snowflake warehouse being used for the notebook session is undersized, leading to memory
• CThe Snowpark ML library encountered an incompatibility issue with the current Snowflake version.
• DThe feature engineering process resulted in an excessively large intermediate dataset that
• EThe notebook is configured to automatically save checkpoints. This feature is causing overhead

Explanation

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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