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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. 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) Monitor the average predicted probability score over time. A significant shift in the average score indicates data drift.
B) Monitor the volume of data processed by the UDF per day. A sudden drop in volume indicates a problem with the data pipeline.
C) 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.
D) 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.
E) Check for null values in the input features passed to the UDF. A sudden increase in null values indicates a problem with data quality.

2. You are developing a Python stored procedure in Snowflake to train a machine learning model using scikit-learn. The training data resides in a Snowflake table named 'SALES DATA. You need to pass the feature columns (e.g., 'PRICE, 'QUANTITY) and the target column ('REVENUE) dynamically to the stored procedure. Which of the following approaches is the MOST secure and efficient way to achieve this, preventing SQL injection vulnerabilities and ensuring data integrity within the stored procedure?

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

3. A data scientist is preparing customer churn data for a machine learning model in Snowflake. The dataset contains a 'Contract_Type' column with values 'Month-to-Month', 'One Year', and 'Two Year'. They want to use label encoding to transform this categorical feature into numerical values. Which of the following SQL statements correctly implements label encoding for the 'Contract_Type' column, assigning 'Month-to-Month' to 0, 'One Year' to 1, and 'Two Year' to 2, and creates a new column named 'Contract_Type_Encoded'? Additionally, the data scientist wants to handle potential NULL values in 'Contract_Type' by assigning them the value of -1.

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

4. You're working with a Snowflake stage named that contains several versions of your machine learning model, named 'model_vl .pkl' , 'model_v2.pkl' , and You want to programmatically list all files in the stage and retrieve the creation time of the latest version (i.e., using SnowSQL. Which of the following approaches is most efficient and correct?

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

5. 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) Implement a SHAP (SHapley Additive exPlanations) analysis on recent transactions to understand feature importance shifts and potential concept drift.
C) Implement a data quality monitoring system to detect anomalies in input features, alongside calculating population stability index (PSI) to quantify data drift.
D) Immediately retrain the model with the latest available data, assuming data drift is the primary issue.
E) Revert to a previous version of the model known to have performed well, while investigating the issue in the background.

Solutions: