Snowflake DSA-C03 Test Passed : SnowPro Advanced: Data Scientist Certification Exam

You have trained a fraud detection model using scikit-learn and want to deploy it in Snowflake using the Snowflake Model Registry. You've registered the model as 'fraud _ model' in the registry. You need to create a Snowflake user-defined function (UDF) that loads and executes the model. Which of the following code snippets correctly creates the UDF, assuming the model is a serialized pickle file stored in a stage named 'model_stage'?

• A. Option B
• B. Option A
• C. Option E
• D. Option C
• E. Option D

You are using Snowflake ML to predict housing prices. You've created a Gradient Boosting Regressor model and want to understand how the 'location' feature (which is categorical, representing different neighborhoods) influences predictions. You generate a Partial Dependence Plot (PDP) for 'location'. The PDP shows significantly different predicted prices for each neighborhood. Which of the following actions would be MOST appropriate to further investigate and improve the model's interpretability and performance?

• A. Remove the 'location' feature from the model, as categorical features are inherently difficult to interpret.
• B. Use one-hot encoding for the 'location' feature and generate individual PDPs for each one-hot encoded column.
• C. Replace the 'location' feature with a numerical feature representing the average house price in each neighborhood, calculated from historical data.
• D. Generate ICE (Individual Conditional Expectation) plots alongside the PDP to assess the heterogeneity of the relationship between 'location' and predicted price.
• E. Combine the PDP for 'location' with a two-way PDP showing the interaction between 'location' and 'square_footage'.

You are working with a large dataset of sensor readings stored in a Snowflake table. You need to perform several complex feature engineering steps, including calculating rolling statistics (e.g., moving average) over a time window for each sensor. You want to use Snowpark Pandas for this task. However, the dataset is too large to fit into the memory of a single Snowpark Pandas worker. How can you efficiently perform the rolling statistics calculation without exceeding memory limits? Select all options that apply.

• A. Explore using Snowpark's Pandas user-defined functions (UDFs) with vectorization to apply custom rolling statistics logic directly within Snowflake. UDFs allow you to use Pandas within Snowflake without needing to bring the entire dataset client-side.
• B. Utilize the 'window' function in Snowpark SQL to define a window specification for each sensor and calculate the rolling statistics using SQL aggregate functions within Snowflake. Leverage Snowpark to consume the results of the SQL transformation.
• C. Increase the memory allocation for the Snowpark Pandas worker nodes to accommodate the entire dataset.
• D. Use the 'grouped' method in Snowpark DataFrame to group the data by sensor ID, then download each group as a Pandas DataFrame to the client and perform the rolling statistics calculation locally. Then upload back to Snowflake.
• E. Break the Snowpark DataFrame into smaller chunks using 'sample' and 'unionAll', process each chunk with Snowpark Pandas, and then combine the results.

You are building a model deployment pipeline using a CI/CD system that connects to your Snowflake data warehouse from your external IDE (VS Code) and orchestrates model training and deployment. The pipeline needs to dynamically create and grant privileges on Snowflake objects (e.g., tables, views, warehouses) required for the model. Which of the following security best practices should you implement when creating and granting privileges within the pipeline?

• A. Hardcode the credentials of a highly privileged user (e.g., a user with the SECURITYADMIN role) in the pipeline script for authentication.
• B. Grant the ' SYSADMIN' role to the service account used by the pipeline to ensure it has sufficient privileges.
• C. Create a custom role with minimal required privileges to perform only the necessary operations for the pipeline, and grant this role to a dedicated service account used by the pipeline.
• D. Grant the 'OWNERSHIP' privilege on all objects to the service account so it can perform any operation.
• E. Use the role within the pipeline script to create and grant all necessary privileges.

You're deploying a pre-built image classification model hosted on a REST API endpoint, and you need to integrate it with Snowflake to classify images stored in cloud storage accessible via an external stage named 'IMAGE STAGE. The API expects image data as a base64 encoded string in the request body. Which SQL query snippet demonstrates the correct approach for calling the external function 'CLASSIFY IMAGE and incorporating the base64 encoding?

• A. Option B
• B. Option A
• C. Option E
• D. Option C
• E. Option D