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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are investigating website session durations stored in a Snowflake table named 'WEB SESSIONS. You suspect that bot traffic is artificially inflating the average session duration. You have the following session durations (in seconds) in the 'SESSION DURATION' column: [10, 12, 15, 18, 20, 22, 25, 28, 30, 1000]. Given this data and the context of bot traffic, which measure of central tendency is MOST robust to the influence of the outlier (1000) in this dataset? Assuming you already have table and dataframe created for this analysis. (Choose ONE)

A) Mean
B) Median
C) Geometric Mean
D) Mode
E) Trimmed mean (e.g. 10% trimmed)

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

3. 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 E
B) Option B
C) Option C
D) Option D
E) Option A

4. You are developing a machine learning model using scikit-learn within Visual Studio Code (VS Code) and connecting directly to Snowflake to access a large dataset. You need to authenticate to Snowflake using Key Pair Authentication, but want to avoid storing the private key directly within your VS Code project or environment variables for security reasons. Which of the following approaches offers the MOST secure way to manage and access the private key for Snowflake authentication from VS Code?

A) Store the private key in a secure vault (e.g., HashiCorp Vault, AWS Secrets Manager, Azure Key Vault) and retrieve it dynamically within your VS Code script using the appropriate API or SDK.
B) Store the private key in a secure database table within Snowflake and query it dynamically.
C) Store the encrypted private key in a configuration file within your VS Code project and decrypt it at runtime using a password-based encryption algorithm.
D) Use the Snowflake CLI to generate a temporary access token and hardcode it into your VS Code script for authentication.
E) Store the private key in a password-protected ZIP archive and extract it during the Snowflake connection process.

5. You are using Snowpark Pandas to prepare data for a machine learning model. You have a Snowpark DataFrame named 'transactions df that contains transaction data, including 'transaction id', 'product id', 'customer id', and 'transaction_amount'. You want to create a new feature that represents the average transaction amount per customer. However, you are concerned about potential skewness in the 'transaction_amount' and want to apply a log transformation to reduce its impact before calculating the average. Which of the following steps using Snowpark Pandas would achieve this transformation and calculation most efficiently within Snowflake?

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

Solutions: