A company has a Python UDF that performs model inference. They want to ensure that only the data science team can call the UDF, while other users can still see the underlying data. What should they do?

Create a view that calls the UDF and grant access to the view.

Make the UDF secure and grant OWNERSHIP to the data science role.

Grant USAGE on the UDF to the data science role only.

Revoke SELECT on the underlying table from all roles.

A team has a vectorized Python UDF for inference. They notice that the UDF runs slower than expected on a large table. They have already increased the warehouse size, but performance is still poor. What is the most likely cause and solution?

The model is too complex; use a simpler model.

The UDF is using a small batch size; increase the batch size.

The warehouse is too large; use a smaller warehouse.

The UDF is not vectorized; rewrite it as a scalar UDF.

Why is it important to track model performance over time?

To improve the model's accuracy automatically

To reduce the cost of inference

To secure the model from unauthorized access

To detect model drift and retrain when necessary

What is the primary advantage of using a vectorized Python UDF in Snowpark for model inference?

It allows the UDF to run on a separate compute cluster.

It automatically scales the model to handle large data volumes.

It processes batches of rows at once, improving performance.

It eliminates the need for a warehouse to run inference.

A team has deployed a vectorized Python UDF for model inference. The inference job on a large table is taking too long. Which action is most likely to improve performance?

Use a smaller warehouse to avoid overloading.

Reduce the batch size in the UDF.

SnowflakeSnowPro Advanced — Data Scientist

Domain 5Objective 1

Deploy Models in Snowflake SNOWPRO-ADVANCED-DATA-SCIENTIST Practice Questions (Page 3)

Part of the Model Deployment and Operationalization domain, which makes up ~24% of our current practice bank.

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Questions 11–15

11application · medium
A company has a Python UDF that performs model inference. They want to ensure that only the data science team can call the UDF, while other users can still see the underlying data. What should they do?
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12expert · hard
A team has a vectorized Python UDF for inference. They notice that the UDF runs slower than expected on a large table. They have already increased the warehouse size, but performance is still poor. What is the most likely cause and solution?
Select an answer first
13foundation · easy
Why is it important to track model performance over time?
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14foundation · easy
What is the primary advantage of using a vectorized Python UDF in Snowpark for model inference?
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15application · medium
A team has deployed a vectorized Python UDF for model inference. The inference job on a large table is taking too long. Which action is most likely to improve performance?
Select an answer first

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