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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are tasked with deploying a fraud detection model in Snowflake using the Model Registry. The model is trained on a dataset that is updated daily. You need to ensure that your deployed model uses the latest approved version and that you can easily roll back to a previous version if any issues arise. Which of the following approaches would provide the most robust and maintainable solution for model versioning and deployment, considering minimal downtime during updates and rollback?

A) Store all model versions within a single model registry entry without versioning, overwriting the existing file with each new training run.
B) Deploy a new Snowflake UDF referencing the model file directly in cloud storage every time the model is retrained. Rely on cloud storage versioning for rollback.
C) Use Snowflake Tasks to periodically refresh a table containing the latest model weights. The UDF directly queries this table for predictions.
D) Register each new model version in the Snowflake Model Registry and promote the desired version to 'PRODUCTION' stage. Update a single UDF that dynamically fetches the model based on the 'PRODUCTION' stage metadata.
E) Create multiple Snowflake UDFs, each corresponding to a different model version. Manually switch the active UDF by updating application code when a new model is deployed.

2. You are a data scientist working with a large dataset of customer transactions stored in Snowflake. You need to identify potential fraud using statistical summaries. Which of the following approaches would be MOST effective in identifying unusual spending patterns, considering the need for scalability and performance within Snowflake?

A) Calculate the average transaction amount and standard deviation for each customer using window functions in SQL. Flag transactions that fall outside of 3 standard deviations from the customer's mean.
B) Sample a subset of the data, calculate descriptive statistics using Snowpark Python and the 'describe()' function, and extrapolate these statistics to the entire dataset.
C) Export the entire dataset to a Python environment, use Pandas to calculate the average transaction amount and standard deviation for each customer, and then identify outliers based on a fixed threshold.
D) Implement a custom UDF (User-Defined Function) in Java to calculate the interquartile range (IQR) for each customer's transaction amounts and flag transactions as outliers if they are below QI - 1.5 IQR or above Q3 + 1.5 IQR.
E) Use Snowflake's native anomaly detection functions (if available, and configured for streaming) to detect anomalies based on transaction amount and frequency, grouped by customer ID.

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

4. You are using Snowpark Feature Store to manage features for your machine learning models. You've created several Feature Groups and now want to consume these features for training a model. To optimize retrieval, you want to use point-in-time correctness. Which of the following actions/configurations are essential to ensure point-in-time correctness when retrieving features using Snowpark Feature Store?

A) Create an associated Stream on the source tables used for Feature Groups
B) Explicitly specify a in the call.
C) Use the method on the Feature Store client, providing a dataframe containing the 'primary_keyS and the desired for each record.
D) Ensure that all source tables used by the Feature Groups have Change Data Capture (CDC) enabled.
E) When creating Feature Groups, specify a 'timestamp_key' that represents the event timestamp of the data in the source tables.

5. You are using a Snowflake Notebook to analyze customer churn for a telecommunications company. You have a dataset with millions of rows and want to perform feature engineering using a combination of SQL transformations and Python code. Your goal is to create a new feature called 'average_monthly call_duration' which calculates the average call duration for each customer over the last 3 months. You are using the Snowpark DataFrame API within your notebook. Given the following code snippet to start with:

A) Option E
B) Option B
C) Option C
D) Option D
E) Option A

Solutions: