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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You have deployed a fraud detection model in Snowflake using Snowpark and are monitoring its performance. You observe a significant drift in the transaction data distribution compared to the data used during training. To address this, you want to implement a retraining strategy. Which of the following steps are MOST critical to automate the retraining process using Snowflake's features?

A) Create a Snowflake Stream on the transaction data table to capture changes since the last training run.
B) Build and deploy a new docker image for each retraining, containing the new model, and update the external function definition to point to the new image.
C) Configure Snowflake's data lineage features to automatically track the input data and model lineage for reproducibility.
D) Develop a Python UDF that periodically calculates drift metrics (e.g., Population Stability Index) and triggers retraining when a threshold is exceeded. Use Snowflake's Task feature to schedule the UDF execution.
E) Replace the existing model artifact in Snowflake's stage with the newly trained model using Snowpark's model registry functionality.

2. You are using Snowpark for Python to build a feature engineering pipeline for a machine learning model that predicts customer churn. The data is stored in a Snowflake table called 'CUSTOMER DATA' , and you want to create new features based on time-series data within the table. You need to calculate the 'Recency' feature (days since the last transaction) and 'Frequency' feature (number of transactions in the last 3 months). Considering performance and best practices, which Snowpark approach would you choose?

A) Use Snowpark DataFrame API to perform window functions within Snowflake to calculate 'Recency' and 'Frequency' directly, leveraging Snowflake's processing power without transferring data to the client.
B) Write a stored procedure in SQL that calculates 'Recency' and 'Frequency' using SQL window functions, and then call this stored procedure from your Snowpark Python code.
C) Fetch the entire 'CUSTOMER DATA table into a Pandas DataFrame using , then use Pandas' time-series functions to calculate 'Recency' and 'Frequency'. After feature engineering, load the Pandas DataFrame back into Snowflake.
D) Create a Python UDF using Pandas to calculate 'Recency' and 'Frequency'. Apply this UDF to the 'CUSTOMER DATA' table through Snowpark, processing the data row by row.
E) Write custom Python code in a Snowpark UDF to retrieve each transaction for a customer and calculate recency and frequency directly in Python without pandas.

3. You are designing a feature engineering pipeline using Snowpark Feature Store for a fraud detection model. You have a transaction table in Snowflake. One crucial feature is the 'average_transaction_amount_last_7_days' for each customer. You want to implement this feature using Snowpark Python and materialize it in the Feature Store. You have the following Snowpark DataFrame 'transactions_df containing 'customer_id' and 'transaction_amount'. Which of the following code snippets correctly defines and registers this feature in the Snowpark Feature Store, ensuring efficient computation and storage?

A)

B)

C)

D)

E)

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

5. You've created a Python stored procedure in Snowflake to train a model. The procedure successfully trains the model, saves it using 'joblib.dump' , and then attempts to upload the model file to an internal stage. However, the upload fails intermittently with a FileNotFoundErroN. The stage is correctly configured, and the stored procedure has the necessary privileges. Which of the following actions are MOST likely to resolve this issue? (Select TWO)

A) Implement error handling within the Python code to catch the 'FileNotFoundError' and retry the file upload after a short delay using 'time.sleep()'. The stored procedure should retry the upload a maximum of 3 times before failing.
B) Before uploading the model to the stage, verify that the file exists using 'os.path.exists()' within the stored procedure. If the file does not exist, log an error and raise an exception.
C) Before uploading the model to the stage, explicitly create the directory within the stage using 'snowflake.connector.connect()' and executing a 'CREATE DIRECTORY IF NOT EXISTS command on the stage. Then retry upload.
D) Use the fully qualified path for the model file when calling 'joblib.dump'. E.g., 'joblib.dump(model, '/tmp/model.joblib')' instead of 'joblib.dump(model, 'model .joblib')'.
E) Ensure that the Python packages used within the stored procedure (e.g., scikit-learn, joblib) are explicitly listed in the 'imports' clause of the 'CREATE PROCEDURE statement.

Solutions: