Snowflake DSA-C03

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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are building a data science pipeline in Snowflake to perform time series forecasting. You've decided to use a Python UDTF to encapsulate the forecasting logic using a library like 'Prophet'. The UDTF needs to access historical data to train the model and generate forecasts. The data is stored in a Snowflake table named 'SALES DATA with columns 'DATE' and 'SALES'. Which of the following approaches is/are most efficient and secure for accessing the 'SALES DATA table from within the UDTF during model training?

A) Create a view on top of 'SALES DATA' and grant access to the UDTF's owner role to the view. Then, query the view using Snowpark within the UDTF.
B) Use the 'snowflake.connector' to connect to Snowflake using a dedicated service account with read-only access to the 'SALES DATA' table. Store the service account credentials securely in Snowflake secrets and retrieve them within the UDTF.
C) Pass the entire 'SALES DATA' table as a Pandas DataFrame to the UDTF as an argument. This approach is suitable for smaller datasets. Do not partition the data frame.
D) Bypass Snowflake entirely and load data from S3 stage into a Pandas dataframe.
E) Use the Snowpark API within the UDTF to query the 'SALES DATA' table directly, leveraging the existing Snowflake session context. This requires no additional credentials management.

2. 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) Use the Snowflake CLI to generate a temporary access token and hardcode it into your VS Code script for authentication.
B) Store the private key in a password-protected ZIP archive and extract it during the Snowflake connection process.
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) 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.
E) Store the private key in a secure database table within Snowflake and query it dynamically.

3. You've built a model in Snowflake to predict house prices based on features like location, square footage, and number of bedrooms. After deploying the model, you want to ensure that the incoming data used for prediction is similar to the data the model was trained on. You decide to implement a data distribution comparison strategy. Consider these options and select all that apply:

A) Generate histograms for each numerical feature in both the training and incoming datasets using a Python UDF that leverages libraries like Pandas and Matplotlib. Visually compare the histograms to identify potential distribution shifts.
B) Use Snowflake's built-in statistics functions to compute quantiles (e.g., 25th, 50th, 75th percentiles) for each numerical feature. Compare these quantiles between the training and incoming datasets and set up alerts for significant deviations.
C) Create a binary classification model in Snowflake that attempts to predict whether a given row of data comes from the training dataset or the incoming dataset. If the model achieves high accuracy, it indicates a significant difference in data distributions.
D) Only focus on monitoring the target variable (house price) and assume that if the distribution of house prices remains stable, the input data distribution is also stable.
E) Calculate the mean and standard deviation for each numerical feature in both the training and incoming datasets using Snowflake SQL. Create a Snowflake Alert that triggers if the difference in means or standard deviations exceeds a predefined threshold for any feature.

4. You are tasked with building a machine learning pipeline in Snowpark Python to predict customer lifetime value (CLTV). You need to access and manipulate data residing in multiple Snowflake tables and views, including customer demographics, purchase history, and website activity. To improve code readability and maintainability, you decide to encapsulate data access and transformation logic within a Snowpark Stored Procedure. Given the following Python code snippet representing a simplified version of your stored procedure:

A) The replace=True, packages=['snowflake-snowpark-python', 'pandas', decorator registers the Python function as a Snowpark Stored Procedure, allowing it to be called from SQL.
B) The 'session.write_pandas(df, table_name='CLTV PREDICTIONS', auto_create_table=Truey function writes the Pandas DataFrame 'df containing the CLTV predictions directly to a new Snowflake table named , automatically creating the table if it does not exist.
C) The 'session.table('CUSTOMER DEMOGRAPHICS')' method creates a local Pandas DataFrame containing a copy of the data from the 'CUSTOMER DEMOGRAPHICS' table.
D) The 'session.sql('SELECT FROM PURCHASE line executes a SQL query against the Snowflake database and returns the results as a list of Row objects.
E) The 'snowflake.snowpark.context.get_active_session()' function retrieves the active Snowpark session object, enabling interaction with the Snowflake database from within the stored procedure.

5. A retail company is using Snowflake to store transaction data'. They want to create a derived feature called 'customer _ recency' to represent the number of days since a customer's last purchase. The transactions table 'TRANSACTIONS has columns 'customer_id' (INT) and 'transaction_date' (DATE). Which of the following SQL queries is the MOST efficient and scalable way to derive this feature as a materialized view in Snowflake?

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

Solutions: