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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are tasked with presenting a business case to stakeholders demonstrating the value of a new machine learning model that predicts customer churn. The model has been trained on data within Snowflake, and you have various metrics such as accuracy, precision, recall, and F I-score. You also have feature importance scores generated using a SHAP (SHapley Additive exPlanations) explainer. Which of the following visualization strategies, when combined, would MOST effectively communicate the model's performance and impact to a non-technical audience, while also providing sufficient detail for technical stakeholders?

A) A confusion matrix visualizing the true positives, true negatives, false positives, and false negatives, along with a summary plot of the SHAP values showing the impact of each feature on the model's prediction for a representative sample of customers. A line chart showing cumulative churn rate across different customer segments.
B) A scatter plot showing the relationship between two key features identified by SHAP, colored by the model's churn prediction, and a table summarizing the model's performance metrics (accuracy, precision, recall, F I-score). Additionally, include a waterfall plot for a specific customer, illustrating how each feature contributes to the final prediction.
C) A simple bar chart showing the overall accuracy score of the model alongside a table detailing the precision, recall, and F I-score. Include a word cloud of the most important features from the SHAP values.
D) A ROC curve (Receiver Operating Characteristic) showing the trade-off between true positive rate and false positive rate, paired with a detailed table of all feature importance scores generated by the SHAP explainer. Present statistical summaries, such as mean and standard deviation, of the top 5 feature values, grouped by predicted churn probability.
E) A distribution plot (e.g., histogram or KDE) of the predicted churn probabilities, segmented by actual churn status (churned vs. not churned), combined with a SHAP force plot visualizing the feature contributions for a single, randomly selected customer who churned. Add a section on potential cost savings from churn reduction.

2. A financial institution wants to predict fraudulent transactions on credit card data stored in Snowflake. The dataset includes features like transaction amount, merchant ID, location, time of day, and user profile information. The target variable is 'is_fraudulent' (0 or 1). You have trained several binary classification models (Logistic Regression, Random Forest, and Gradient Boosting) using scikit-learn and persisted them using a Snowflake external function for inference. To optimize for both performance (inference speed) and accuracy, which of the following steps should you consider before deploying your model for real-time scoring using the external function? SELECT ALL THAT APPLY.

A) Increase the batch size of requests sent to the external function to amortize the overhead of invoking the external function itself, even if it increases latency for individual transactions.
B) Replace the trained models with a simple rule-based system based solely on transaction amount. If the amount is greater than a threshold, flag it as fraudulent, as this will be faster than calling the external function.
C) Normalize or standardize the input features in Snowflake using SQL before passing them to the external function to ensure consistent scaling and potentially improve model performance.
D) Evaluate the models on a representative held-out dataset within Snowflake using SQL queries (e.g., calculating AUC, precision, recall) to choose the model with the best balance of performance and accuracy before deploying it.
E) Implement feature selection techniques (e.g., using feature importance scores from Random Forest or Gradient Boosting) to reduce the number of features passed to the external function, improving inference speed.

3. You have successfully trained a binary classification model using Snowpark ML and deployed it as a UDF in Snowflake. The UDF takes several input features and returns the predicted probability of the positive class. You need to continuously monitor the model's performance in production to detect potential data drift or concept drift. Which of the following methods and metrics, when used together, would provide the MOST comprehensive and reliable assessment of model performance and drift in a production environment? (Select TWO)

A) Continuously calculate and track performance metrics like AUC, precision, recall, and Fl-score on a representative sample of labeled production data over regular intervals. Compare these metrics to the model's performance on the holdout set during training.
B) Calculate the Kolmogorov-Smirnov (KS) statistic between the distribution of predicted probabilities in the training data and the production data over regular intervals. Track any substantial changes in the KS statistic.
C) Check for null values in the input features passed to the UDF. A sudden increase in null values indicates a problem with data quality.
D) Monitor the volume of data processed by the UDF per day. A sudden drop in volume indicates a problem with the data pipeline.
E) Monitor the average predicted probability score over time. A significant shift in the average score indicates data drift.

4. You are tasked with developing a multi-class image classification model to categorize product images stored in Snowflake external stage. The categories are 'Electronics', 'Clothing', 'Furniture', 'Books', and 'Food'. You plan to use a pre-trained Convolutional Neural Network (CNN) model and fine-tune it using your dataset. However, you're facing challenges in efficiently loading and preprocessing the image data within the Snowflake environment before feeding it to your model. Which of the following approaches would be MOST efficient for image data loading and preprocessing in Snowflake, minimizing data movement and leveraging Snowflake's scalability, for a large dataset exceeding 1 TB of images?

A) Use Snowflake's Snowpark to read images from the external stage into a Snowpark DataFrame. Then, implement image preprocessing using Snowpark DataFrame operations, such as resizing and normalization, within the DataFrame transformations before sending the data to the model.
B) Utilize Snowflake's external function integration with AWS Lambda to preprocess images as they are uploaded to S3, storing the preprocessed data back in S3 and creating an external table pointing to the preprocessed data.
C) Download all the images from the external stage to a local machine, preprocess them using a standard Python library like OpenCV, and then upload the processed data back into Snowflake as a table for model training.
D) Write a Python User-Defined Function (UDF) that loads each image from the external stage directly into memory, performs preprocessing (resizing, normalization), and returns the processed image data. The UDF is then called in a SQL query to process the image data.
E) Create a Snowflake Stream to continuously ingest new images into a Snowflake table. Use a task to periodically trigger a Python UDF that preprocesses the newly ingested images and stores them in another table for model training.

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) Create features that capture the trend of customer activity over time (e.g., increasing or decreasing activity) using LACY and 'LEAD' window functions.
B) Create a feature representing the number of days since the customer's last login using "DATEDIFF and window functions.
C) 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.
D) Directly use the ACTIVITY TYPE column as a categorical feature without any transformation or engineering.
E) Calculate the recency, frequency, and monetary value (RFM) for each customer using window functions and aggregate functions.

Solutions: