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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are a data scientist working for a retail company using Snowflake. You're building a linear regression model to predict sales based on advertising spend across various channels (TV, Radio, Newspaper). After initial EDA, you suspect multicollinearity among the independent variables. Which of the following Snowflake SQL statements or techniques are MOST appropriate for identifying and addressing multicollinearity BEFORE fitting the model? Choose two.

A) Calculate the Variance Inflation Factor (VIF) for each independent variable using a user-defined function (UDF) in Snowflake that implements the VIF calculation based on R-squared values from auxiliary regressions. This requires fitting a linear regression for each independent variable against all others.
B) Use ' on each independent variable to estimate its uniqueness. If uniqueness is low, multicollinearity is likely.
C) Implement Principal Component Analysis (PCA) using Snowpark Python to transform the independent variables into uncorrelated principal components and then select only the components explaining a certain percentage of the variance.
D) Drop one of the independent variable randomly if they seem highly correlated.
E) Generate a correlation matrix of the independent variables using 'CORR aggregate function in Snowflake SQL and examine the correlation coefficients. Values close to +1 or -1 suggest high multicollinearity.

2. You've trained a sentiment analysis model in Snowflake using Snowpark Python and deployed it as a UDF. After several weeks, you notice the model's performance has degraded significantly. You suspect concept drift. Which of the following actions represent the MOST effective and comprehensive approach to address this situation, considering the entire Machine Learning Lifecycle, including monitoring, retraining, and model versioning? Assume you have monitoring in place that alerted you to the drift.

A) Adjust the existing model's parameters manually to compensate for the observed performance degradation without retraining or versioning.
B) Disable the model and revert to a rule-based system, abandoning the machine learning approach altogether.
C) Analyze the recent data to understand the nature of the concept drift, retrain the model with a combination of historical and recent data, version the new model, and perform AIB testing against the existing model before fully deploying the new version. Log both model version predictions during AIB testing.
D) Retrain the model on a sample of the most recent data, overwriting the original model files in your Snowflake stage and updating the UDF definition. Keep no record of the old model.
E) Immediately replace the current UDF with a newly trained model using the latest data, ignoring model versioning and assuming the latest data will solve the drift issue.

3. You are developing a Python stored procedure in Snowflake to predict sales for a retail company. You want to incorporate external data (e.g., weather forecasts) into your model. Which of the following methods are valid and efficient ways to access and use external data within your Snowflake Python stored procedure?

A) Load the external data into a Snowflake table and then query the table from within the Python stored procedure using the Snowflake Connector for Pythom
B) Directly call external APIs within the Python stored procedure using libraries like 'requests'. Snowflake's network policy must be configured to allow outbound connections.
C) Embed the external data directly into the Python stored procedure's code as a dictionary or JSON object.
D) Use a Snowflake external function to pre-process the external data and then pass the processed data as input parameters to the Python stored procedure.
E) Use a Snowflake Pipe to continuously ingest external data from a cloud storage location and access the data within the stored procedure.

4. You're working on a fraud detection system for an e-commerce platform. You have a table 'TRANSACTIONS with a 'TRANSACTION AMOUNT column. You want to bin the transaction amounts into several risk categories ('Low', 'Medium', 'High', 'Very High') using explicit boundaries. You want the bins to be inclusive of the lower boundary and exclusive of the upper boundary (e.g., [0, 100), [100, 500), etc.). Which of the following SQL statements using the 'WIDTH BUCKET function correctly bins the transaction amounts into these categories, assuming these boundaries: 0, 100, 500, 1000, and infinity, and assigns appropriate labels?

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

5. A data scientist is using association rule mining with the Apriori algorithm on customer purchase data in Snowflake to identify product bundles. After generating the rules, they obtain the following metrics for a specific rule: Support = 0.05, Confidence = 0.7, Lift = 1.2. Consider that the overall purchase probability of the consequent (right-hand side) of the rule is 0.4. Which of the following statements are CORRECT interpretations of these metrics in the context of business recommendations for product bundling?

A) Customers who purchase the items in the antecedent are 70% more likely to also purchase the items in the consequent, compared to the overall purchase probability of the consequent.
B) The lift value of 1.2 suggests a strong negative correlation between the antecedent and consequent, indicating that purchasing the antecedent items decreases the likelihood of purchasing the consequent items.
C) The confidence of 0.7 indicates that 70% of transactions containing the antecedent also contain the consequent.
D) The lift value of 1.2 indicates that customers are 20% more likely to purchase the consequent items when they have also purchased the antecedent items, compared to the baseline purchase probability of the consequent items.
E) The rule applies to 5% of all transactions in the dataset, meaning 5% of the transactions contain both the antecedent and the consequent.

Solutions: