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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. 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 rule applies to 5% of all transactions in the dataset, meaning 5% of the transactions contain both the antecedent and the consequent.
B) 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.
C) 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.
D) The confidence of 0.7 indicates that 70% of transactions containing the antecedent also contain the consequent.
E) 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.

2. You are building a machine learning pipeline that uses data stored in Snowflake. You want to connect a Jupyter Notebook running on your local machine to Snowflake using Snowpark. You need to securely authenticate to Snowflake and ensure that you are using a dedicated compute resource for your Snowpark session. Which of the following approaches is the MOST secure and efficient way to achieve this?

A) Use key pair authentication to connect to Snowflake, storing the private key securely on your local machine. Specify a dedicated virtual warehouse during session creation.
B) Configure OAuth authentication for your Snowflake account and use the OAuth token to establish a Snowpark session with a dedicated virtual warehouse.
C) Store your Snowflake username and password directly in the Jupyter Notebook and create a Snowpark session using these credentials and the default Snowflake warehouse.
D) Use the Snowflake Python connector with username and password and execute SQL commands to create a Snowpark DataFrame.
E) Hardcode a role with 'ACCOUNTADMIN' privileges in your Jupyter Notebook using username and password.

3. A data scientist needs to calculate the cumulative moving average of sales for each product in a table. The table contains columns: (INT), (DATE), and (NUMBER). The desired output should include the product_id', 'sale_date', and Which of the following Snowflake SQL statements correctly calculates the cumulative moving average for each product using window functions?

A) SELECT product_id, sale_date, daily_sales, OVER (PARTITION BY product_id ORDER BY sale_date ASC ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS cumulative_average FROM
B) SELECT product_id, sale_date, daily_sales, OVER (PARTITION BY product_id ORDER BY sale_date AS cumulative_average FROM sales_by_day;
C) SELECT product_id, sale_date, daily_sales, AVG(daily_sales) OVER (PARTITION BY product_id) AS cumulative_average FROM sales_by_day;
D) SELECT product_id, sale_date, daily_sales, OVER (PARTITION BY product_id ORDER BY sale_date ASC) / OVER (PARTITION BY product_id ORDER BY sale_date ASC) AS cumulative_average FROM sales_by_day;
E) SELECT product_id, sale_date, daily_sales, AVG(daily_sales) OVER (ORDER BY sale_date ASC) AS cumulative_average FROM sales_by_day;

4. A data scientist is tasked with predicting house prices using Snowflake. They have a dataset stored in a Snowflake table called 'HOUSE PRICES' with columns such as 'SQUARE FOOTAGE, 'NUM BEDROOMS, 'LOCATION_ID, and 'PRICE. They choose a Random Forest Regressor model. Which of the following steps is MOST important to prevent overfitting and ensure good generalization performance on unseen data, and how can this be effectively implemented within a Snowflake-centric workflow?

A) Eliminate outliers without understanding the data properly to reduce noise.
B) Tune the hyperparameters of the Random Forest model (e.g., 'max_deptm, 'n_estimators') using cross-validation. You can achieve this by splitting the 'HOUSE PRICES table into training and validation sets using Snowflake's 'QUALIFY clause or temporary tables, then train and evaluate the model within a loop or stored procedure.
C) Randomly select a small subset of the features (e.g., only use 'SQUARE FOOTAGE and 'NUM BEDROOMS) to simplify the model and prevent overfitting.
D) Train the Random Forest model on the entire 'HOUSE PRICES table without splitting into training and validation sets, as this will provide the model with the most data.
E) Increase the number of estimators (trees) in the Random Forest to the maximum possible value to capture all potential patterns, without cross validation.

5. 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) Directly call external APIs within the Python stored procedure using libraries like 'requests'. Snowflake's network policy must be configured to allow outbound connections.
B) Use a Snowflake Pipe to continuously ingest external data from a cloud storage location and access the data within the stored procedure.
C) Embed the external data directly into the Python stored procedure's code as a dictionary or JSON object.
D) 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
E) 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.

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