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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are developing a regression model in Snowflake using Snowpark to predict house prices based on features like square footage, number of bedrooms, and location. After training the model, you need to evaluate its performance. Which of the following Snowflake SQL queries, used in conjunction with the model's predictions stored in a table named 'PREDICTED PRICES, would be the most efficient way to calculate the Root Mean Squared Error (RMSE) using Snowflake's built-in functions, given that the actual prices are stored in the 'ACTUAL PRICES' table?

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

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) 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.
B) 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.
C) 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.
D) 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.
E) 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.

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

4. You are developing a Snowflake Native App that leverages Snowflake Cortex for text summarization. The app needs to process user-provided text input in real-time and return a summarized version. You want to expose this functionality as a secure and scalable REST API endpoint within the Snowflake environment. Which of the following strategies are MOST suitable for achieving this, considering best practices for security and performance?

A) Develop a Snowflake Native App containing a Python UDF that calls 'SNOWFLAKCORTEX.SUMMARIZE function, and expose it as a REST API endpoint using Snowflake's API Integration feature within the app package.
B) Create a Snowflake External Function using Python that directly calls the 'SNOWFLAKE.CORTEX.SUMMARIZE' function and expose this function via a REST API gateway outside of Snowflake.
C) Write a Snowflake Stored Procedure using Javascript to invoke the 'SNOWFLAKE.CORTEX.SUMMARIZE function, deploy the procedure to a Snowflake stage, and then trigger it via an AWS Lambda function integrated with Snowflake.
D) Utilize a Snowflake Stored Procedure written in SQL that invokes the 'SNOWFLAKE.CORTEX.SUMMARIZE' function, and then create a Snowflake API Integration to expose the stored procedure as a REST endpoint.
E) Develop a Snowflake Native App that includes a Java UDF that calls 'SNOWFLAKE.CORTEX.SUMMARIZE and expose a REST API using Snowflake's built-in REST API capabilities within the Native App framework.

5. You are tasked with predicting sales (SALES AMOUNT') for a retail company using linear regression in Snowflake. The dataset includes features like 'ADVERTISING SPEND', 'PROMOTIONS', 'SEASONALITY INDEX', and 'COMPETITOR PRICE'. After training a linear regression model named 'sales model', you observe that the model performs poorly on new data, indicating potential issues with multicollinearity or overfitting. Which of the following strategies, applied directly within Snowflake, would be MOST effective in addressing these issues and improving the model's generalization performance? Choose ALL that apply.

A) Manually remove highly correlated features (e.g., if 'ADVERTISING SPEND and 'PROMOTIONS' have a correlation coefficient above 0.8) based on a correlation matrix calculated using 'CORR function and feature selection techniques.
B) Increase the size of the training dataset significantly by querying data from external sources.
C) Decrease the 'MAX_ITERATIONS' parameter in the 'CREATE MODEL' statement to prevent the model from overfitting to the training data.
D) Apply Ridge Regression by adding an L2 regularization term during model training. This can be achieved by setting the 'REGULARIZATION' parameter of the 'CREATE MODEL' statement to 'L2'.
E) Perform feature scaling (e.g., standardization or min-max scaling) on the input features before training the model, using Snowflake's built-in functions or user-defined functions (UDFs) for scaling.

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