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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

Question 1

You are tasked with training a model within Snowflake to predict customer churn for a telecommunications company. The dataset is stored in a Snowflake table named 'CUSTOMER DATA. The features include 'age', and 'data_usage'. The target variable is 'churned' (boolean). You want to use the SNOWFLAKE.ML.ANACONDA INTEGRATION to leverage Scikit-learn for model training. Which of the following code snippets correctly performs model training with Snowflake ML, addressing potential issues like feature scaling and data type handling within the stored procedure?

A.

B.

C.

D.

Question 2

You are tasked with building a fraud detection model using Snowflake and Snowpark Python. The model needs to identify fraudulent transactions in real-time with high precision, even if it means missing some actual fraud cases. Which combination of optimization metric and model tuning strategy would be most appropriate for this scenario, considering the importance of minimizing false positives (incorrectly flagging legitimate transactions as fraudulent)?

A. F 1-Score, optimized to balance precision and recall equally.
B. Log Loss, optimized with a grid search focusing on hyperparameters that improve overall accuracy.
C. Precision, optimized with a threshold adjustment to minimize false positives.
D. AUC-ROC, optimized with a randomized search focusing on hyperparameters related to model complexity.
E. Recall, optimized with a threshold adjustment to minimize false negatives.

Question 3

A marketing analyst at 'NovaRetail' suspects that a new advertising campaign has increased the average purchase amount. They have historical purchase data in a Snowflake table called 'purchase_historf. To validate their hypothesis using the Central Limit Theorem (CLT), they perform the following steps: 1. Calculate the population mean (?) of purchase amounts from the historical data'. 2. Draw 500 random samples of size 50 from the table. 3. Calculate the sample mean (x?) for each sample. Which of the following steps are essential for correctly applying the Central Limit Theorem to perform a z-test to determine whether the new advertising campaign has significantly increased the average purchase amount?

A. Calculate the standard deviation of the population (?) from the historical data and estimate the standard error of the mean as ? / sqrt(50).
B. Ensure that the samples are drawn independently and randomly.
C. Check if the original population distribution (purchase amounts) is approximately normally distributed.
D. Verify that the sample size (n=50) is sufficiently large to approximate normality of the sample mean distribution based on the CLT. This implicitly assumes population size is significantly larger than the sample size.
E. Calculate the standard deviation of the sample means and use it as an estimate for the standard error of the mean.

Question 4

Which of the following statements about Z-tests and T-tests are generally true? Select all that apply.

A. A T-test has fewer degrees of freedom compared to the Z-test, making it more robust to outliers.
B. Both Z-tests and T-tests assume that the data is non-normally distributed.
C. A T-test is generally used when the sample size is large (n > 30) and the population standard deviation is known.
D. A Z-test requires knowing the population standard deviation, while a T-test estimates it from the sample data.
E. As the sample size increases, the T-distribution approaches the standard normal (Z) distribution.

Question 5

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. 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.
B. 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.
C. Store all model versions within a single model registry entry without versioning, overwriting the existing file with each new training run.
D. 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.
E. Use Snowflake Tasks to periodically refresh a table containing the latest model weights. The UDF directly queries this table for predictions.

Solutions: