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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are using a Snowflake Notebook to build a churn prediction model. You have engineered several features, and now you want to visualize the relationship between two key features: and , segmented by the target variable 'churned' (boolean). Your goal is to create an interactive scatter plot that allows you to explore the data points and identify any potential patterns.
Which of the following approaches is most appropriate and efficient for creating this visualization within a Snowflake Notebook?

A) Use the 'snowflake-connector-python' to pull the data and use 'seaborn' to create static plots.
B) Use the Snowflake Connector for Python to fetch the data, then leverage a Python visualization library like Plotly or Bokeh to generate an interactive plot within the notebook.
C) Write a stored procedure in Snowflake that generates the visualization data in a specific format (e.g., JSON) and then use a JavaScript library within the notebook to render the visualization.
D) Leverage Snowflake's native support for Streamlit within the notebook to create an interactive application. Query the data directly from Snowflake within the Streamlit app and use Streamlit's plotting capabilities for visualization.
E) Create a static scatter plot using Matplotlib directly within the Snowflake Notebook by converting the data to a Pandas DataFrame. This involves pulling all relevant data into the notebook's environment before plotting.

2. You are developing a fraud detection model in Snowflake. You've identified that transaction amounts and transaction frequency are key features. You observe that the transaction amounts are heavily right-skewed and the transaction frequencies have outliers. Furthermore, the model needs to be robust against seasonal variations in transaction frequency. Which of the following feature engineering steps, when applied in sequence, would be MOST appropriate to handle these data characteristics effectively?

A) 1. Apply a logarithmic transformation to the transaction amounts. 2. Apply a Winsorization technique to the transaction frequencies to handle outliers. 3. Calculate a rolling average of transaction frequency over a 7-day window.
B) 1. Apply a Box-Cox transformation to the transaction amounts. 2. Apply a quantile-based transformation (e.g., using NTILE) to the transaction frequencies to map them to a uniform distribution. 3. Calculate the difference between the current transaction frequency and the average transaction frequency for that day of the week over the past year.
C) 1. Apply a logarithmic transformation to the transaction amounts. 2. Replace outliers in transaction frequency with the mean value. 3. Create lag features of transaction frequency for the previous 7 days.
D) 1. Apply a square root transformation to the transaction amounts. 2. Standardize the transaction frequencies using Z-score normalization. 3. Create dummy variables for the day of the week.
E) 1. Apply min-max scaling to the transaction amounts. 2. Remove outliers in transaction frequency using the Interquartile Range (IQR) method. 3. Calculate the cumulative sum of transaction frequencies.

3. You've created a Python stored procedure in Snowflake to train a model. The procedure successfully trains the model, saves it using 'joblib.dump' , and then attempts to upload the model file to an internal stage. However, the upload fails intermittently with a FileNotFoundErroN. The stage is correctly configured, and the stored procedure has the necessary privileges. Which of the following actions are MOST likely to resolve this issue? (Select TWO)

A) Before uploading the model to the stage, verify that the file exists using 'os.path.exists()' within the stored procedure. If the file does not exist, log an error and raise an exception.
B) Ensure that the Python packages used within the stored procedure (e.g., scikit-learn, joblib) are explicitly listed in the 'imports' clause of the 'CREATE PROCEDURE statement.
C) Before uploading the model to the stage, explicitly create the directory within the stage using 'snowflake.connector.connect()' and executing a 'CREATE DIRECTORY IF NOT EXISTS command on the stage. Then retry upload.
D) Use the fully qualified path for the model file when calling 'joblib.dump'. E.g., 'joblib.dump(model, '/tmp/model.joblib')' instead of 'joblib.dump(model, 'model .joblib')'.
E) Implement error handling within the Python code to catch the 'FileNotFoundError' and retry the file upload after a short delay using 'time.sleep()'. The stored procedure should retry the upload a maximum of 3 times before failing.

4. You are managing a machine learning model lifecycle in Snowflake using the Model Registry. Which of the following statements are true regarding model lineage and governance when utilizing the Model Registry for model versioning and deployment?

A) Model Registry automatically retrains models based on scheduled data updates, ensuring models are always up-to-date without manual intervention.
B) Integration with Snowflake's RBAC (Role-Based Access Control) allows for granular control over who can register, update, and deploy model versions.
C) The Model Registry automatically tracks the exact SQL queries used to train the model, allowing for full reproducibility of the training process.
D) Custom tags and metadata can be associated with each model version, enabling detailed documentation and traceability of model development and deployment.
E) The Model Registry provides a central repository to register, version, and manage models, enabling better collaboration and governance across data science teams.

5. You are tasked with automating the retraining of a Snowpark ML model based on the performance metrics of the deployed model. You have a table 'MODEL PERFORMANCE that stores daily metrics like accuracy, precision, and recall. You want to automatically trigger retraining when the accuracy drops below a certain threshold (e.g., 0.8). Which of the following approaches using Snowflake features and Snowpark ML is the MOST robust and cost-effective way to implement this automated retraining pipeline?

A) Create a Dynamic Table that depends on the 'MODEL PERFORMANCE table and materializes when the accuracy is below the threshold. This Dynamic Table refresh triggers a Snowpark ML model training stored procedure. This stored procedure saves the new model with a timestamp and updates a metadata table with the model's details.
B) Use a Snowflake stream on the 'MODEL_PERFORMANCE table to detect changes in accuracy, and trigger a Snowpark ML model training function using a PIPE whenever the accuracy drops below the threshold.
C) Implement an external service (e.g., AWS Lambda or Azure Function) that periodically queries the "MODEL_PERFORMANCE table using the Snowflake Connector and triggers a Snowpark ML model training script via the Snowflake API.
D) Create a Snowflake task that runs every hour, queries the 'MODEL_PERFORMANCE table, and triggers a Snowpark ML model training script if the accuracy threshold is breached. The training script will overwrite the existing model.
E) Implement a Snowpark ML model training script that automatically retrains the model every day, regardless of the performance metrics. This script will overwrite the previous model.

Solutions: