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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You're developing a Python UDTF in Snowflake to perform sentiment analysis on customer reviews. The UDTF uses a pre-trained transformer model from Hugging Face. The code is as follows:

When deploying this UDTF, you encounter a 'ModuleNotFoundError: No module named 'transformers" error. Considering best practices for managing dependencies in Snowflake UDTFs, what is the most effective way to resolve this issue?

A) Upload all the dependencies of Transformers (manually downloaded libraries) to the internal stage.
B) Create a Conda environment containing the 'transformers' library, package it into a zip file, upload it to a Snowflake stage, and specify the stage path in the 'imports' parameter when registering the UDTF.
C) Use the 'snowflake-ml-python' library and its dependency management features to automatically resolve and deploy the 'transformers' dependency.
D) Install the 'transformers' library directly on the Snowflake compute nodes using Snowpark's 'add_packageS method at the session level:
E) Include the 'transformers' library in the same Python file as the UDTF definition. This is acceptable for smaller libraries.

2. 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) 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.
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) Use the 'snowflake-connector-python' to pull the data and use 'seaborn' to create static plots.

3. You have developed a customer churn prediction model using Python and deployed it as a Snowflake UDE You are monitoring its performance and notice a significant drop in accuracy over time. To address this, you need to implement automated model retraining with regular validation. Which of the following steps and validation techniques are MOST critical for ensuring the retrained model is effective and avoids overfitting to recent data? (Select THREE)

A) Implement a data drift detection mechanism. Monitor the distribution of input features over time and trigger retraining if significant drift is detected using tools such as Snowflake's Anomaly Detection features or custom drift metrics calculated in SQL.
B) Update the UDF in place using 'CREATE OR REPLACE FUNCTION' immediately after retraining completes, regardless of the validation results.
C) Retrain the model using the entire available dataset, as this will maximize the amount of data the model learns from.
D) Monitor the model's performance on a live dataset and trigger retraining only when the performance drops below a predefined threshold, using metrics like accuracy, precision, or recall. Save Model Performance to 'MODEL_PERFORMANCE.
E) Use cross-validation techniques (e.g., k-fold cross-validation) during the retraining process to estimate the model's performance on unseen data and prevent overfitting. Evaluate on a held-out validation set.

4. You have deployed a fraud detection model in Snowflake, predicting fraudulent transactions. Initial evaluations showed high accuracy. However, after a few months, the model's performance degrades significantly. You suspect data drift and concept drift. Which of the following actions should you take FIRST to identify and address the root cause?

A) Increase the model's prediction threshold to reduce false positives, even if it means potentially missing more fraudulent transactions.
B) Immediately retrain the model with the latest available data, assuming data drift is the primary issue.
C) Implement a SHAP (SHapley Additive exPlanations) analysis on recent transactions to understand feature importance shifts and potential concept drift.
D) Implement a data quality monitoring system to detect anomalies in input features, alongside calculating population stability index (PSI) to quantify data drift.
E) Revert to a previous version of the model known to have performed well, while investigating the issue in the background.

5. A data scientist is tasked with creating features for a machine learning model predicting customer churn. They have access to the following data in a Snowflake table named 'CUSTOMER ID, 'DATE, 'ACTIVITY _ TYPE' (e.g., 'login', 'purchase', 'support_ticket'), and 'ACTIVITY VALUE (e.g., amount spent, duration of login). Which of the following feature engineering strategies, leveraging Snowflake's capabilities, could be useful for predicting customer churn? (Select all that apply)

A) Directly use the ACTIVITY TYPE column as a categorical feature without any transformation or engineering.
B) Calculate the recency, frequency, and monetary value (RFM) for each customer using window functions and aggregate functions.
C) Create features that capture the trend of customer activity over time (e.g., increasing or decreasing activity) using LACY and 'LEAD' window functions.
D) Use 'APPROX COUNT DISTINCT to estimate the number of unique product categories purchased by each customer within the last 3 months to create a features.
E) Create a feature representing the number of days since the customer's last login using "DATEDIFF and window functions.

Solutions: