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Snowflake DSA-C03 Exam Overview:

Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are building a time-series forecasting model in Snowflake to predict the hourly energy consumption of a building. You have historical data with timestamps and corresponding energy consumption values. You've noticed significant daily seasonality and a weaker weekly seasonality. Which of the following techniques or approaches would be most appropriate for capturing both seasonality patterns within a supervised learning framework using Snowflake?

A) Using Fourier terms (sine and cosine waves) with frequencies corresponding to daily and weekly cycles as features in a regression model.
B) Using a simple moving average to smooth the data before applying a linear regression model.
C) Creating lagged features (e.g., energy consumption from the previous hour, the same hour yesterday, and the same hour last week) and using these features as input to a regression model (e.g., Random Forest or Gradient Boosting).
D) Decomposing the time series using STL (Seasonal-Trend decomposition using Loess) and building separate models for the trend and seasonal components, then combining the predictions.
E) Applying exponential smoothing directly to the original time series without feature engineering.

2. You're developing a Python UDTF in Snowflake to perform sentiment analysis on customer reviews. The UDTF uses a pre-trained transformer model from Hugging Face. The code is as follows:

When deploying this UDTF, you encounter a 'ModuleNotFoundError: No module named 'transformers" error. Considering best practices for managing dependencies in Snowflake UDTFs, what is the most effective way to resolve this issue?

A) Install the 'transformers' library directly on the Snowflake compute nodes using Snowpark's 'add_packageS method at the session level:
B) Create a Conda environment containing the 'transformers' library, package it into a zip file, upload it to a Snowflake stage, and specify the stage path in the 'imports' parameter when registering the UDTF.
C) Use the 'snowflake-ml-python' library and its dependency management features to automatically resolve and deploy the 'transformers' dependency.
D) Include the 'transformers' library in the same Python file as the UDTF definition. This is acceptable for smaller libraries.
E) Upload all the dependencies of Transformers (manually downloaded libraries) to the internal stage.

3. You are tasked with deploying a fraud detection model in Snowflake using the Model Registry. The model is trained on a dataset that is updated daily. You need to ensure that your deployed model uses the latest approved version and that you can easily roll back to a previous version if any issues arise. Which of the following approaches would provide the most robust and maintainable solution for model versioning and deployment, considering minimal downtime during updates and rollback?

A) Store all model versions within a single model registry entry without versioning, overwriting the existing file with each new training run.
B) Create multiple Snowflake UDFs, each corresponding to a different model version. Manually switch the active UDF by updating application code when a new model is deployed.
C) Use Snowflake Tasks to periodically refresh a table containing the latest model weights. The UDF directly queries this table for predictions.
D) Register each new model version in the Snowflake Model Registry and promote the desired version to 'PRODUCTION' stage. Update a single UDF that dynamically fetches the model based on the 'PRODUCTION' stage metadata.
E) Deploy a new Snowflake UDF referencing the model file directly in cloud storage every time the model is retrained. Rely on cloud storage versioning for rollback.

4. You are using the Snowflake Python connector from within a Jupyter Notebook running in VS Code to train a model. You have a Snowflake table named 'CUSTOMER DATA' with columns 'ID', 'FEATURE 1', 'FEATURE_2, and 'TARGET. You want to efficiently load the data into a Pandas DataFrame for model training, minimizing memory usage. Which of the following code snippets is the MOST efficient way to achieve this, assuming you only need 'FEATURE 1', 'FEATURE 2, and 'TARGET' columns?

A)

B)

C)

D)

E)

5. You are building a machine learning pipeline in Snowflake using Snowpark Python. You have completed the data preparation and feature engineering steps and now need to train a model. You want to track the performance of different model versions and hyperparameters using MLflow. You are considering these deployment strategies. Which of the deployment strategies allows automatic logging of metrics, parameters, and model artifacts to MLflow for each training run without requiring explicit MLflow logging code?

A) Train the model using Snowpark's DataFrame API directly in a Snowflake worksheet. Manually create a log file with metrics and model parameters and upload it to a Snowflake stage.
B) Train the model within a Snowpark Python stored procedure. Use a Snowflake stage to store MLflow artifacts.
C) Use the Snowpark MLAPI and its integration with MLflow's autologging feature. Enable autologging before starting the training run. Deploy the model to Snowflake as a UDF.
D) Train the model locally on your development machine and manually log metrics and artifacts to MLflow using the MLflow API. Then, deploy the trained model to Snowflake as a UDF or stored procedure.
E) Train the model within a Snowpark Python UDF. Use a Snowflake stage to store MLflow artifacts.

Solutions: