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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. 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) 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.
B) 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.
C) 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.
D) 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.
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.

2. You are analyzing a dataset of website traffic and conversions in Snowflake, aiming to understand the relationship between the number of pages visited CPAGES VISITED) and the conversion rate (CONVERSION_RATE). You perform a simple linear regression using the 'REGR SLOPE and 'REGR INTERCEPT functions. However, after plotting the data and the regression line, you observe significant heteroscedasticity (non-constant variance of errors). Which of the following actions, performed within Snowflake during the data preparation and feature engineering phase, are MOST appropriate to address this heteroscedasticity and improve the validity of your linear regression model? (Select all that apply)

A) Remove outlier data points from the dataset based on the Interquartile Range (IQR) of the residuals from the original linear regression model. This requires calculating the residuals first.
B) Apply a logarithmic transformation to the 'CONVERSION RATE' variable using the 'LN()' function. CREATE OR REPLACE VIEW TRANSFORMED_DATA AS SELECT PAGES VISITED, LN(CONVERSION RATE) AS LOG_CONVERSION RATE FROM ORIGINAL_DATA;
C) Standardize the 'PAGES_VISITED' and 'CONVERSION_RATE variables using the and functions.Create OR REPLACE VIEW STANDARDIZED_DATA AS SELECT (PAGES_VISITED - OVER()) / OVER() AS Z PAGES_VISITED, (CONVERSION RATE -OVER()) / OVER() AS FROM ORIGINAL_DATA;
D) Apply a Box-Cox transformation to the 'CONVERSION RATE' variable. This transformation will determine the optimal lambda value using some complex SQL statistical operations. This can be approximated to log tranformation in many real life scenarios.
E) Calculate the weighted least squares regression by weighting each observation by the inverse of the squared predicted values from an initial OLS regression. This requires multiple SQL queries.

3. You are working with a Snowflake table named 'sensor readingS containing IoT sensor data'. The table has columns 'sensor id' , 'timestamp' , and 'reading value'. You observe that the 'reading value' column contains a significant number of missing values (represented as NULL). To prepare this data for a time series analysis, you need to impute these missing values. You have decided to use the 'LOCF' (Last Observation Carried Forward) method, filling the NULL values with the most recent non-NULL value for each sensor. In addition to LOCF, you also want to handle the scenario where a sensor has NULL values at the beginning of its data stream (i.e., no previous observation to carry forward). For these initial NULLs, you want to use a fixed default value of 0. Which of the following approaches, using either Snowpark for Python or a combination of Snowpark and SQL, correctly implements this LOCF imputation with a default value?

A) All of the above
B)

C)

D)

E)

4. You're developing a model to predict customer churn using Snowflake. Your dataset is large and continuously growing. You need to implement partitioning strategies to optimize model training and inference performance. You consider the following partitioning strategies: 1. Partitioning by 'customer segment (e.g., 'High-Value', 'Medium-Value', 'Low-Value'). 2. Partitioning by 'signup_date' (e.g., monthly partitions). 3. Partitioning by 'region' (e.g., 'North America', 'Europe', 'Asia'). Which of the following statements accurately describe the potential benefits and drawbacks of these partitioning strategies within a Snowflake environment, specifically in the context of model training and inference?

A) Using clustering in Snowflake on top of partitioning will always improve query performance significantly and reduce compute costs irrespective of query patterns.
B) Partitioning by 'customer_segment' is beneficial if churn patterns are significantly different across segments, allowing for training separate models for each segment. However, if any segment has very few churned customers, it may lead to overfitting or unreliable models for that segment.
C) Partitioning by 'signup_date' is ideal for capturing temporal dependencies in churn behavior and allows for easy retraining of models with the latest data. It also naturally aligns with a walk-forward validation approach. However, it might not be effective if churn drivers are independent of signup date.
D) Partitioning by 'region' is useful if churn is heavily influenced by geographic factors (e.g., local market conditions). It can improve query performance during both training and inference when filtering by region. However, it can create data silos, making it difficult to build a global churn model that considers interactions across regions. Furthermore, the 'region' column must have low cardinality.
E) Implementing partitioning requires modifying existing data loading pipelines and may introduce additional overhead in data management. If the cost of partitioning outweighs the performance gains, it's better to rely on Snowflake's built-in micro-partitioning alone. Also, data skew in partition keys is a major concern.

5. 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) Create a view that selects only the non-PII columns for model training. Grant access to this view to the data science team.
B) 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.
C) 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.
D) 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.
E) Encrypt the entire database containing the transaction data to protect PII from unauthorized access.

Solutions: