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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

Question 1

You've trained a model using Snowflake ML and want to deploy it for real-time predictions using a Snowflake UDF. To ensure minimal latency, you need to optimize the UDF's performance. Which of the following strategies and considerations are most important when creating and deploying a UDF for model inference in Snowflake to minimize latency, especially when the model is large (e.g., > 100MB)?
Select all that apply.

A. Utilize a Snowflake external function instead of a UDF if the model requires access to resources outside of Snowflake's environment.
B. Ensure the UDF code is written in Python and utilizes vectorized operations with libraries like NumPy to process data in batches efficiently.
C. Use a Snowflake Stage to store the model file and load the model within the UDF using 'snowflake.snowpark.files.SnowflakeFile' to minimize memory footprint.
D. Store the trained model as a BLOB within the UDF code itself to avoid external dependencies.
E. Use smaller warehouse size for UDF evaluation in order to reduce latency and compute costs.

Question 2

A data science team is using Snowpark ML to train a classification model. They want to log model metadata (e.g., training parameters, evaluation metrics) and artifacts (e.g., the serialized model file) for reproducibility and model governance purposes. Which of the following approaches is the most appropriate for integrating model logging and artifact management within the Snowpark ML workflow, minimizing operational overhead?

A. Only track basic model performance metrics in a Snowflake table and rely on code versioning (e.g., Git) for model artifact management.
B. Serialize the model object to a string and store it as a VARIANT column in a Snowflake table, alongside the model metadata.
C. Leverage the MLflow integration within Snowpark, utilizing its ability to track experiments, log parameters and metrics, and store model artifacts directly within Snowflake stages or external storage.
D. Employ a separate, external model management platform (e.g., Databricks MLflow, SageMaker Model Registry) and configure Snowpark to interact with it via API calls during model training and deployment.
E. Use a custom Python function to manually write model metadata to a Snowflake table and store the model file in a Snowflake stage.

Question 3

You're working with a large dataset of user transactions in Snowflake. You need to identify potential outliers in transaction amounts C TRANSACTION AMOUNT) for each user CUSER ID'). Your goal is to flag transactions that are more than 3 standard deviations away from the mean transaction amount for that specific user. Which of the following approaches, utilizing Snowflake's statistical functions and window functions, would be MOST efficient and accurate for achieving this?

A. Calculating the overall mean and standard deviation for all transactions and filtering transactions based on those global statistics.
B. Exporting the data to a Python environment, performing the calculations using Pandas, and then re-importing the results to Snowflake.
C. Using window functions to calculate the mean and standard deviation for each user within the same query, and then comparing each transaction amount to the calculated range.
D. Using a correlated subquery to calculate the mean and standard deviation for each user and then filtering the transactions.
E. Creating a stored procedure that iterates through each user and calculates the mean and standard deviation individually.

Question 4

A data science team is tasked with deploying a pre-built anomaly detection model in Snowflake to identify fraudulent transactions. They need to use Snowflake ML functions and a Snowflake Native App (that houses the model) to achieve this. The Snowflake Native App is installed and available. The transaction data is stored in a table called 'TRANSACTIONS. Which of the following steps are essential to successfully deploy and use this pre-built model within a User Defined Function (UDF) for real-time scoring, assuming the app provides a function named 'ANOMALY SCORE?

A. Create an external function in API Integration instead of UDF.
B. Grant the USAGE privilege on the Snowflake Native App to the role executing the UDF. This ensures the UDF can access the app's functionality.
C. Train the pre-built anomaly detection model using Snowflake's ML functions (e.g., 'CREATE MODELS) with the 'TRANSACTIONS' data before creating the UDE
D. Create a UDF that calls the 'ANOMALY _ SCORE function provided by the Snowflake Native App, passing the relevant transaction features as arguments.
E. Ensure the 'TRANSACTIONS' table is shared with the Snowflake Native App's container so the model can directly access the data.

Question 5

A pharmaceutical company is testing a new drug to lower blood pressure. They conduct a clinical trial with 200 patients. After treatment, the sample mean reduction in systolic blood pressure is 10 mmHg, with a sample standard deviation of 15 mmHg. You want to construct a 99% confidence interval for the true mean reduction in systolic blood pressure. Which of the following statements is most accurate concerning the appropriate distribution and critical value to use?

A. Use a z-distribution because the sample size is large (n > 30), and the critical value is approximately 2.576.
B. Use a t-distribution with 199 degrees of freedom, and the critical value is slightly larger than 2.576.
C. Use a chi-squared distribution with 199 degrees of freedom.
D. Use a z-distribution because we are estimating mean, and use a critical value of 1.96.
E. Use a t-distribution with 200 degrees of freedom, and the critical value is close to 2.576.

Solutions: