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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. A data scientist is performing exploratory data analysis on a table named 'CUSTOMER TRANSACTIONS. They need to calculate the standard deviation of transaction amounts C TRANSACTION AMOUNT) for different customer segments CCUSTOMER SEGMENT). The 'CUSTOMER SEGMENT column can contain NULL values. Which of the following SQL statements will correctly compute the standard deviation, excluding NULL transaction amounts, and handling NULL customer segments by treating them as a separate segment called 'Unknown'? Consider using Snowflake-specific functions where appropriate.

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

2. You have successfully deployed a machine learning model in Snowflake using Snowpark and are generating predictions. You need to implement a robust error handling mechanism to ensure that if the model encounters an issue during prediction (e.g., missing feature, invalid data type), the process doesn't halt and the errors are logged appropriately. You are using a User-Defined Function (UDF) to call the model. Which of the following strategies, when used IN COMBINATION, provides the BEST error handling and monitoring capabilities in this scenario?

A) Implement a custom logging solution by writing error messages to an external file storage (e.g., AWS S3) using an external function called from within the UDE
B) Rely solely on Snowflake's query history to identify failed predictions and debug the model, without any explicit error handling within the UDE
C) Use a 'TRY...CATCH' block within the UDF to catch exceptions, log the errors to a separate Snowflake table, and return a default prediction value (e.g., NULL) for the affected row.
D) Use Snowflake's event tables to capture errors and audit logs related to the UDF execution.
E) Wrap the prediction call in a 'SYSTEM$QUERY_PROFILE function to get detailed query execution statistics and identify potential performance bottlenecks.

3. 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) Mode
D) Geometric Mean
E) Trimmed mean (e.g. 10% trimmed)

4. You have deployed a regression model in Snowflake as an external function using AWS Lambda'. The external function takes several numerical features as input and returns a predicted value. You want to continuously monitor the model's performance in production and automatically retrain it when the performance degrades below a predefined threshold. Which of the following methods represent VALID approaches for calculating and monitoring model performance within the Snowflake environment and triggering the retraining process?

A) Implement custom logging within the AWS Lambda function to capture prediction results and actual values. Configure AWS CloudWatch to monitor these logs and trigger an AWS Step Function that initiates a new training job and updates the Snowflake external function with the new model endpoint upon completion.
B) Utilize Snowflake's Alerting feature, setting an alert rule based on the output of a SQL query that calculates performance metrics. Configure the alert action to invoke a webhook that triggers a retraining pipeline.
C) Build a Snowpark Python application deployed on Snowflake which periodically polls the external function's performance by querying the function with a sample data set and comparing results to ground truth stored in Snowflake. Initiate retraining directly from the Snowpark application if performance degrades.
D) Create a view that joins the input features with the predicted output and the actual result. Configure model monitoring within the AWS Sagemaker to perform continuous validation of the model.
E) Create a Snowflake Task that periodically executes a SQL query to calculate performance metrics (e.g., RMSE) by comparing predicted values from the external function with actual values stored in a separate table. Trigger a Python UDF, deployed as a Snowflake stored procedure, to initiate retraining if the RMSE exceeds the threshold.

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 within a Snowpark Python UDF. Use a Snowflake stage to store MLflow artifacts.
B) 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.
C) 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.
D) Train the model within a Snowpark Python stored procedure. Use a Snowflake stage to store MLflow artifacts.
E) 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.

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