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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are building a time-series forecasting model in Snowflake to predict the hourly energy consumption of a building. You have historical data with timestamps and corresponding energy consumption values. You've noticed significant daily seasonality and a weaker weekly seasonality. Which of the following techniques or approaches would be most appropriate for capturing both seasonality patterns within a supervised learning framework using Snowflake?

A) Creating lagged features (e.g., energy consumption from the previous hour, the same hour yesterday, and the same hour last week) and using these features as input to a regression model (e.g., Random Forest or Gradient Boosting).
B) Decomposing the time series using STL (Seasonal-Trend decomposition using Loess) and building separate models for the trend and seasonal components, then combining the predictions.
C) Using a simple moving average to smooth the data before applying a linear regression model.
D) Applying exponential smoothing directly to the original time series without feature engineering.
E) Using Fourier terms (sine and cosine waves) with frequencies corresponding to daily and weekly cycles as features in a regression model.

2. You are deploying a time series forecasting model in Snowflake. You need to log the performance metrics (e.g., MAE, RMSE) of the model after each prediction run to the Snowflake Model Registry. Which of the following steps are necessary to achieve this?

A) Use the method with the 'metrics' parameter to log the metrics directly during model registration.
B) Create a separate table in Snowflake to store the performance metrics and use SQL "INSERT statements to log the metrics after each prediction run.
C) Leverage Snowflake's Event Tables to capture and store metrics data generated during model evaluation and prediction workflows and then access via stored procedures that log to the Model Registry.
D) You must create a custom logging solution outside of Snowflake using external services and then integrate those logs back into Snowflake via external functions and Model Registry APIs
E) Use the method to log individual metrics to the Model Registry associated with a specific model version after the prediction run.

3. You are developing a machine learning model within a Snowflake UDF (User-Defined Function) written in Python. This UDF needs to access external Python libraries not included in the default Snowflake Anaconda channel. You've created a stage and uploaded the necessary file. You've successfully used 'conda create' and 'conda install --file requirements.txt' to create your environment locally, and subsequently zipped the environment. Now, what steps are essential to configure the Snowflake UDF to correctly use these external libraries from the stage? Select all that apply.

A) Create a ZIP file containing the Python environment and upload it to a Snowflake stage.
B) Install the packages directly into the Snowflake environment using 'CREATE OR REPLACE FUNCTION RETURNS VARCHAR ..: and a pip install command within the function.
C) Specify the stage path containing the zipped environment in the 'imports' clause of the 'CREATE OR REPLACE FUNCTION' statement using the symbol and specifying the zip file e.g., '@snowflake_packages/myenv.zip'.
D) Include the line 'import sys; sys._xoptions['snowflake_home'] = at the top of your UDF to point to the environment stage location.
E) Set the 'PYTHON_VERSION' parameter of the 'CREATE OR REPLACE FUNCTION' statement to match the Python version used in your environment using e.g. 'PYTHON_VERSION = '3.8".

4. A data scientist is building a model in Snowflake to predict customer churn. They have a dataset with features like 'age', 'monthly_spend', 'contract_length', and 'complaints'. The target variable is 'churned' (0 or 1). They decide to use a Logistic Regression model. However, initial performance is poor. Which of the following actions could MOST effectively improve the model's performance, considering best practices for Supervised Learning in a Snowflake environment focused on scalable and robust deployment?

A) Fit a deep neural network with numerous layers directly within Snowflake without any data preparation, as this will automatically extract complex patterns.
B) Implement feature scaling (e.g., StandardScaler or MinMaxScaler) on numerical features within Snowflake, before training the model. Leverage Snowflake's user-defined functions (UDFs) for transformation and then train the model.
C) Increase the learning rate significantly to speed up convergence during training.
D) Ignore missing values in the dataset as the Logistic Regression model will handle it automatically without skewing the results.
E) Reduce the number of features by randomly removing some columns, as this always prevents overfitting.

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) 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.
B) The confidence of 0.7 indicates that 70% of transactions containing the antecedent also contain the consequent.
C) 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.
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: