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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You have deployed a fraud detection model in Snowflake using Snowpark and are monitoring its performance. You observe a significant drift in the transaction data distribution compared to the data used during training. To address this, you want to implement a retraining strategy. Which of the following steps are MOST critical to automate the retraining process using Snowflake's features?

A) Build and deploy a new docker image for each retraining, containing the new model, and update the external function definition to point to the new image.
B) Replace the existing model artifact in Snowflake's stage with the newly trained model using Snowpark's model registry functionality.
C) Develop a Python UDF that periodically calculates drift metrics (e.g., Population Stability Index) and triggers retraining when a threshold is exceeded. Use Snowflake's Task feature to schedule the UDF execution.
D) Create a Snowflake Stream on the transaction data table to capture changes since the last training run.
E) Configure Snowflake's data lineage features to automatically track the input data and model lineage for reproducibility.

2. You are tasked with predicting sales (SALES AMOUNT') for a retail company using linear regression in Snowflake. The dataset includes features like 'ADVERTISING SPEND', 'PROMOTIONS', 'SEASONALITY INDEX', and 'COMPETITOR PRICE'. After training a linear regression model named 'sales model', you observe that the model performs poorly on new data, indicating potential issues with multicollinearity or overfitting. Which of the following strategies, applied directly within Snowflake, would be MOST effective in addressing these issues and improving the model's generalization performance? Choose ALL that apply.

A) Manually remove highly correlated features (e.g., if 'ADVERTISING SPEND and 'PROMOTIONS' have a correlation coefficient above 0.8) based on a correlation matrix calculated using 'CORR function and feature selection techniques.
B) Decrease the 'MAX_ITERATIONS' parameter in the 'CREATE MODEL' statement to prevent the model from overfitting to the training data.
C) Apply Ridge Regression by adding an L2 regularization term during model training. This can be achieved by setting the 'REGULARIZATION' parameter of the 'CREATE MODEL' statement to 'L2'.
D) Perform feature scaling (e.g., standardization or min-max scaling) on the input features before training the model, using Snowflake's built-in functions or user-defined functions (UDFs) for scaling.
E) Increase the size of the training dataset significantly by querying data from external sources.

3. You've deployed a regression model in Snowflake to predict product sales. After a month, you observe that the RMSE on your validation dataset has increased significantly compared to the initial deployment. Analyzing the prediction errors, you notice a pattern: the model consistently underestimates sales for products with a recent surge in social media mentions. Which of the following actions would be MOST effective in addressing this issue and improving the model's RMSE?

A) Retrain the model using only the most recent data (e.g., last week) to adapt to the changing sales patterns.
B) Incorporate a feature representing the number of social media mentions for each product into the model and retrain.
C) Decrease the learning rate of the optimization algorithm during retraining to avoid overshooting the optimal weights.
D) Implement a moving average smoothing technique on the target variable (sales) before retraining the model.
E) Increase the regularization strength of the model to prevent overfitting to the original training data.

4. You've deployed a fraud detection model in Snowflake. The model is implemented as a Python UDF that uses a pre-trained scikit-learn model stored as a stage file. Your goal is to enable near real-time fraud detection on incoming transactions. Due to regulatory requirements, you need to maintain a detailed audit trail of all predictions, including the input features, model version, prediction scores, and any errors encountered during the prediction process. Which of the following approaches are valid and efficient for storing these audit logs and predictions in Snowflake?

A) Use Snowflake's 'SYSTEM$QUERY LOG' table to extract information about the UDF execution and join it with the transaction data to reconstruct the audit trail.
B) Store the audit logs as unstructured text files in an external stage (e.g., AWS S3) and periodically load them into a Snowflake table using COPY INTO command.
C) Log the audit information to an external logging service (e.g., Splunk) using an external function called from within the UDF.
D) Create a dedicated table with columns for transaction ID, input features (as a JSON VARIANT), model version, prediction score, error message (if any), and prediction timestamp. Use a Snowflake Sequence to generate unique log IDs.
E) Utilize Snowflake's Streams and Tasks to automatically capture changes to the transaction table and trigger the prediction UDF, storing the audit logs in a separate table with similar structure as described in option A.

5. A data scientist is developing a model within a Snowpark Python environment to predict customer churn. They have established a Snowflake session and loaded data into a Snowpark DataFrame named 'customer data'. The feature engineering pipeline requires a custom Python function, 'calculate engagement_score', to be applied to each row. This function takes several columns as input and returns a single score representing customer engagement. The data scientist wants to apply this function in parallel across the entire DataFrame using Snowpark's UDF capabilities. The following code snippet is used to define and register the UDF:

When the UDF is called the above error is observed. What change needs to be applied to make the UDF work as expected?

A) Wrap the Python function inside a stored procedure using @F.sproc' and call that stored procedure instead of the plain python function.
B) Change the function call to use the Snowpark DataFrame's 'select' function with column objects: 'customer_data.select(engagement_score_udf(F.col('num_transactions'), F.col('avg_transaction_value'),
C) Remove argument from 'session.udf.register' call. Snowpark can infer the input types automatically.
D) Redefine the function to accept string arguments and cast them to the correct data types within the function.
E) Add '@F.sproc' decorator before the function definition.

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