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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are performing exploratory data analysis on a dataset of customer transactions in Snowflake to prepare for a linear regression model that predicts transaction value based on several customer-related features (e.g., age, location, number of previous transactions). You suspect a non-linear relationship between 'customer_age' and 'transaction_value'. Which of the following Snowflake SQL techniques is MOST appropriate for exploring and potentially transforming the 'customer_age' variable to better fit a linear regression model?

A) Calculate the Pearson correlation coefficient between 'customer_age' and 'transaction_value' using the function. If the correlation is low, discard the 'customer_age' variable.
B) Use the window function to bin 'customer_age' into quartiles and treat each quartile as a categorical variable in the linear regression model.
C) Create polynomial features by adding 'customer_ageA2' and 'customer_ageA3' as new columns to the table, without checking for interaction effects.
D) Apply a logarithmic transformation to 'customer_age' if a scatter plot of 'customer_age' vs 'transaction_value' shows a curve that flattens out as 'customer_age' increases.
E) Implement a Box-Cox transformation in Snowpark Python, select a suitable transformation parameter based on the data, and apply the transformation on 'customer_age' feature.

2. You are using Snowflake ML to predict housing prices. You've created a Gradient Boosting Regressor model and want to understand how the 'location' feature (which is categorical, representing different neighborhoods) influences predictions. You generate a Partial Dependence Plot (PDP) for 'location'. The PDP shows significantly different predicted prices for each neighborhood. Which of the following actions would be MOST appropriate to further investigate and improve the model's interpretability and performance?

A) Generate ICE (Individual Conditional Expectation) plots alongside the PDP to assess the heterogeneity of the relationship between 'location' and predicted price.
B) Remove the 'location' feature from the model, as categorical features are inherently difficult to interpret.
C) Replace the 'location' feature with a numerical feature representing the average house price in each neighborhood, calculated from historical data.
D) Combine the PDP for 'location' with a two-way PDP showing the interaction between 'location' and 'square_footage'.
E) Use one-hot encoding for the 'location' feature and generate individual PDPs for each one-hot encoded column.

3. You are tasked with forecasting the daily sales of a specific product for the next 30 days using Snowflake. You have historical sales data for the past 3 years, stored in a Snowflake table named 'SALES DATA', with columns 'SALE DATE (DATE type) and 'SALES AMOUNT' (NUMBER type). You want to use the Prophet library within a Snowflake User-Defined Function (UDF) for forecasting. The Prophet model requires the input data to have columns named 'ds' (for dates) and 'y' (for values). Which of the following code snippets demonstrates the CORRECT way to prepare and pass your data to the Prophet UDF in Snowflake, assuming you've already created the Python UDF 'prophet_forecast'?

A)

B)

C)

D)

E)

4. You are building a fraud detection model using transaction data stored in Snowflake. The dataset includes features like transaction amount, merchant category, location, and time. Due to regulatory requirements, you need to ensure personally identifiable information (PII) is handled securely and compliantly during the data collection and preprocessing phases. Which of the following combinations of Snowflake features and techniques would be MOST suitable for achieving this goal?

A) Apply differential privacy techniques on aggregated data derived from the transaction data, before using it for model training. Combine this with Snowflake's row access policies to restrict access to sensitive transaction records based on user roles and data attributes.
B) Encrypt the entire database containing the transaction data to protect PII from unauthorized access.
C) Use Snowflake's masking policies to redact PII columns before any data is accessed for model training. Ensure role-based access control is configured so that only authorized personnel can access the unmasked data for specific purposes.
D) Use Snowflake's data sharing capabilities to share the transaction data with a third-party machine learning platform for model development, without any PII masking or redaction.
E) Create a view that selects only the non-PII columns for model training. Grant access to this view to the data science team.

5. A retail company, 'GlobalMart,' wants to optimize its product placement strategy in its physical stores. They have transactional data stored in Snowflake, capturing which items are purchased together in the same transaction. They aim to use association rule mining to identify frequently co-occurring items. Given the following simplified transactional data in a Snowflake table named 'SALES TRANSACTIONS:

Which of the following SQL-based approaches, combined with Snowpark Python for association rule generation (using a library like 'mlxtend'), would be the MOST efficient and scalable way to prepare this data for association rule mining, specifically focusing on converting it into a transaction-item matrix suitable for algorithms like Apriori? Assume 'spark' is a 'snowpark.Session' object connected to your Snowflake environment.

A) Utilizing Snowflake's SQL function within a stored procedure to concatenate items purchased in each transaction into a string, then processing the string using Python in Snowpark to create the transaction-item matrix. This approach minimizes data transfer but introduces string parsing overhead in Python.
B) Using Snowpark's 'DataFrame.groupBy(V and functions to aggregate items by transaction ID, then pivoting the data using to create the transaction-item matrix. This approach requires loading all data into the Snowpark DataFrame before pivoting.
C) Creating a temporary table in Snowflake using a SQL query that aggregates items by transaction and represents them in a format suitable for Snowpark's 'mlxtend' library. Then load this temporary table into a Snowpark DataFrame and use it as input to the Apriori algorithm.
D) First extracting all the data from snowflake into pandas dataframe and then use pivoting and other pandas operations to convert to the needed format.
E) Employing a custom UDF (User-Defined Function) written in Java or Scala that directly processes the transactional data within Snowflake and outputs the transaction-item matrix in a format suitable for Snowpark. This offloads processing to compiled code within Snowflake, maximizing performance.

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