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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are tasked with feature engineering a dataset containing customer transaction data stored in a Snowflake table named 'CUSTOMER TRANSACTIONS'. This table includes columns like 'CUSTOMER ID', 'TRANSACTION DATE, and 'TRANSACTION AMOUNT. You need to create a new feature representing the 'Recency' of the customer, which is the number of days since their last transaction. Using Snowpark Pandas, which of the following code snippets will correctly calculate the Recency feature as a new column in a Snowpark DataFrame?

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

2. A data scientist is developing a fraud detection model using Snowpark ML on Snowflake. They have a feature engineering pipeline implemented as a Snowpark DataFrame transformation. The pipeline includes several complex UDFs. The data scientist observes that the pipeline execution is slow. What are the most effective techniques to optimize the feature engineering pipeline's performance in Snowpark?

A) Cache intermediate DataFrames using or 'persist()' to avoid recomputation of common transformations.
B) Replace Python UDFs with Snowflake SQL UDFs where possible, as SQL UDFs often offer better performance due to Snowflake's optimization capabilities.
C) Rewrite Python UDFs as vectorized Python UDFs using the 'pandas' API within Snowpark to leverage batch processing.
D) Disable Snowpark's lazy evaluation by executing on the DataFrame after each transformation.
E) Reduce the size of the input DataFrame by sampling the data.

3. You are tasked with deploying a pre-trained sentiment analysis model hosted externally using AWS SageMaker. The model endpoint requires an API key for authentication, and you want to score customer reviews stored in a Snowflake table named 'CUSTOMER REVIEWS. Which of the following steps are necessary to securely and efficiently integrate this external model with Snowflake, assuming you have already created a Snowflake stage to store secrets?

A) Use Snowflake's external functions to directly call the SageMaker endpoint from a SQL query, passing the customer review text as input. No separate secure external stage configuration is needed as long as Snowflake has internet access.
B) Create an external function in Snowflake that invokes the SageMaker endpoint, hardcoding the API key directly into the function definition for simplicity.
C) Create an external function in Snowflake that retrieves the API key from a secure Snowflake secret object. Grant USAGE privilege on the secret to the service account associated with the external function.
D) Store the API key in an environment variable within the AWS Lambda function (if using API Gateway) that serves as an intermediary between Snowflake and SageMaker. Snowflake calls the API Gateway endpoint which relays the request to the SageMaker endpoint, and no specific configuration is needed on snowflake.
E) Create a secret object in Snowflake to store the API key. Grant appropriate privileges on the secret to the role that will execute the external function. Modify external function that references secure external stage.

4. You are using Snowflake Cortex to perform sentiment analysis on customer reviews stored in a table called 'CUSTOMER REVIEWS' The table has a column containing the text of each review. You want to create a user-defined function (UDF) to extract sentiment score between the range of -1 to 1 using the 'snowflake_cortex.sentiment' function in Snowflake Cortex. Which of the following UDF definitions would correctly implement this, allowing it to be called directly on the column?

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

5. You're building a linear regression model in Snowflake to predict house prices. You have the following features: 'square_footage', 'number of bedrooms', 'location id', and 'year built'. 'location id' is a categorical variable representing different neighborhoods. You suspect that the relationship between 'square footage' and 'price' might differ based on the 'location id'. Which of the following approaches in Snowflake are BEST suited to explore and model this potential interaction effect?

A) Fit separate linear regression models for each unique 'location_id', using 'square_footage', 'number_of_bedrooms', and 'year_built' as independent variables.
B) Apply a power transformation to 'square_footage' before including it in the linear regression model. This correct, but only to one variable.
C) Create interaction terms by multiplying 'square_footage' with one-hot encoded columns derived from 'location_id'. Include these interaction terms in the linear regression model.
D) Create interaction terms by adding 'square_footage' and one-hot encoded columns derived from 'location_id'. Include these interaction terms in the linear regression model.
E) Use the 'QUALIFY clause in Snowflake SQL to filter the data based on 'location_id' before calculating regression coefficients. This is incorrect approach.

Solutions: