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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are deploying a large language model (LLM) to Snowflake using a user-defined function (UDF). The LLM's model file, '11m model.pt', is quite large (5GB). You've staged the file to Which of the following strategies should you employ to ensure successful deployment and efficient inference within Snowflake? Select all that apply.

A) Split the large model file into smaller chunks and stage each chunk separately. Reassemble the model within the UDF code before inference.
B) Use the 'PUT' command with to compress the model file before staging it. Snowflake will automatically decompress it during UDF execution.
C) Use the 'IMPORTS' clause in the UDF definition to reference Ensure the UDF code loads the model lazily (i.e., only when it's first needed) to minimize startup time and memory usage.
D) Leverage Snowflake's Snowpark Container Services to deploy the LLM as a separate containerized application and expose it via a Snowpark API. Then call that endpoint from snowflake.
E) Increase the warehouse size to XLARGE or larger to provide sufficient memory for loading the large model into the UDF environment.

2. You have trained a complex machine learning model using Snowpark for Python and are now preparing it for production deployment using Snowpark Container Services. You have containerized the model and pushed it to a Snowflake-managed registry. However, you need to ensure that only authorized users can access and deploy this model. Which of the following actions MUST you take to secure your model in the Snowflake Model Registry, ensuring appropriate access control, and minimizing the risk of unauthorized deployment or modification?

A) Grant the 'READ privilege on the container registry to all users who need to deploy the model. Create a custom role with the 'APPLY MASKING POLICY privilege and grant this role to the deployment team.
B) Store the model outside of Snowflake managed registry and use external authentication to control access.
C) Grant the 'USAGE privilege on the database and schema containing the model registry, grant the 'READ privilege on the registry itself, and grant the EXECUTE TASK' privilege to the deployment team for the deployment task.
D) Grant the 'USAGE privilege on the stage where the model files are stored to all users who need to deploy the model.
E) Create a custom role, grant the USAGE' privilege on the database and schema containing the model registry, grant the 'READ privilege on the registry, and then grant this custom role to only those users authorized to deploy the model. Consider masking sensitive model parameters using masking policies.

3. You are evaluating a binary classification model built in Snowflake for predicting customer churn. You have access to the model's predictions on a holdout dataset, and you want to use both the ROC curve and the confusion matrix to comprehensively assess its performance. Which of the following statements regarding the interpretation and use of ROC curves and confusion matrices are correct in this scenario?

A) The area under the ROC curve (AUC) provides a single scalar value representing the overall discriminatory power of the model, with a higher AUC indicating better performance. An AUC of 0.5 indicates that the model performs no better than random chance.
B) While the ROC curve is independent of the class distribution, the metrics derived from the confusion matrix (e.g., precision, recall) can be significantly affected by imbalanced datasets.
C) The confusion matrix allows you to calculate precision, recall, F I-score, and accuracy, which are all useful for understanding the model's performance in terms of correctly and incorrectly classified instances.
D) The ROC curve visualizes the trade-off between true positive rate (sensitivity) and false negative rate (1 - specificity) at various threshold settings.
E) In Snowflake, you can generate ROC curves and confusion matrices directly using the 'SYSTEM$PREDICT function with appropriate parameters and visualizing the results using a tool like Snowsight or Tableau.

4. You have a regression model deployed in Snowflake predicting customer churn probability, and you're using RMSE to monitor its performance. The current production RMSE is consistently higher than the RMSE you observed during initial model validation. You suspect data drift is occurring. Which of the following are effective strategies for monitoring, detecting, and mitigating this data drift to improve RMSE? (Select TWO)

A) Regularly re-train the model on the entire historical dataset to ensure it captures all possible data patterns.
B) Randomly sample a large subset of the production data and manually compare it to the original training data to identify any differences.
C) Use Snowflake's data lineage features to identify any changes in the upstream data sources feeding the model and assess their potential impact.
D) Disable model monitoring, because the increased RMSE shows that the model is adapting to new patterns.
E) Implement a process to continuously calculate and track the RMSE on a holdout dataset representing the most recent data, alerting you when the RMSE exceeds a predefined threshold.

5. A retail company is using Snowflake to store sales data'. They have a table called 'SALES DATA' with columns: 'SALE ID', 'PRODUCT D', 'SALE DATE', 'QUANTITY' , and 'PRICE'. The data scientist wants to analyze the trend of daily sales over the last year and visualize this trend in Snowsight to present to the business team. Which of the following approaches, using Snowsight and SQL, would be the most efficient and appropriate for visualizing the daily sales trend?

A) Use the Snowsight web UI to manually filter the 'SALES_DATX table by 'SALE_DATE for the last year and create a bar chart showing 'SALE_ID count per day.
B) Write a SQL query that calculates the daily total sales amount CSUM(QUANTITY PRICEY) for the last year and use Snowsight's charting options to generate a line chart with 'SALE DATE on the x-axis and daily sales amount on the y-axis.
C) Create a Snowflake view that aggregates the daily sales data, then use Snowsight to visualize the view data as a table without any chart.
D) Write a SQL query that uses 'DATE TRUNC('day', SALE DATE)' to group sales by day and calculate the total sales (SUM(QUANTITY PRICE)). Use Snowsight's line chart option with the truncated date on the x-axis and total sales on the y-axis, filtering by 'SALE_DATE' within the last year. Furthermore, use moving average with window function to smooth the data.
E) Export all the data from the 'SALES DATA' table to a CSV file and use an external tool like Python's Matplotlib or Tableau to create the visualization.

Solutions: