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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are building a model to predict loan defaults using a dataset stored in Snowflake. After training your model and calculating residuals, you create a scatter plot of the residuals against the predicted values. The plot shows a cone-shaped pattern, with residuals spreading out more as the predicted values increase. Which of the following SQL queries, run within a Snowpark Python session, could be used to address the underlying issue indicated by this residual pattern, assuming the predicted values are stored in a column named and the residuals in a column named 'loan_default_residuar in a Snowflake table named 'loan_predictionds'?

A)

B)

C)

D)

E)

2. You have trained a complex Random Forest model in Snowflake to predict loan default risk. You wish to understand the individual and combined effects of 'credit_score' and 'debt_to_income_ratio' on the predicted probability of default. Which approach is MOST suitable for visualizing and interpreting these relationships?

A) Fit a simpler linear model (e.g., Logistic Regression) to the data and interpret its coefficients.
B) Generate individual Partial Dependence Plots (PDPs) for 'credit_score' and 'debt_to_income_ratio'.
C) Create a two-way Partial Dependence Plot (PDP) showing the interaction between 'credit_score' and 'debt_to_income_ratio'.
D) Calculate feature importance using SNOWFLAKE.ML.FEATURE IMPORTANCE and focus on the features with the highest scores.
E) Examine the model's overall accuracy (e.g., AUC) and assume the relationships are well-represented.

3. You are responsible for deploying a fraud detection model in Snowflake. The model needs to be validated rigorously before being put into production. Which of the following actions represent the MOST comprehensive approach to model validation within the Snowflake environment, focusing on both statistical performance and operational readiness, and using Snowflake features for validation?

A) Performing a single train/test split of the historical data and evaluating model performance metrics (e.g., accuracy, precision, recall) on the test set using standard Python libraries within a Snowflake Snowpark environment. Deploying the model directly if the metrics exceed a predefined threshold.
B) Calculating only the AUC (Area Under the Curve) metric on the entire dataset without performing any data splitting or cross-validation. Deploying the model if the AUC is above 0.7.
C) Relying on a simple visual inspection of model outputs and comparing them to a small sample of known fraud cases. Skipping formal validation to accelerate the deployment process.
D) Implementing K-fold cross-validation using Snowflake stored procedures and temporary tables to store and aggregate the results from each fold. Evaluating the model's performance across different data segments and time periods to assess its robustness. Using Snowflake streams and tasks to automate the validation process on new incoming data.
E) Conducting a comprehensive backtesting analysis using historical data, simulating real-world scenarios, and evaluating the model's performance under different conditions. Using Snowflake's time travel feature to access historical data snapshots for accurate backtesting. Monitoring model performance using Snowflake alerts triggered by custom SQL queries against model prediction logs.

4. A data scientist is tasked with building a predictive maintenance model for industrial equipment. The data is collected from IoT sensors and stored in Snowflake. The raw sensor data is voluminous and contains noise, outliers, and missing values. Which of the following code snippets, executed within a Snowflake environment, demonstrates the MOST efficient and robust approach to cleaning and transforming this sensor data during the data collection phase, specifically addressing outlier removal and missing value imputation using robust statistics? Assume necessary libraries like numpy and pandas are available via Snowpark.

A)

B)

C)

D)

E)

5. 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 B
B) Option D
C) Option E
D) Option A
E) Option C

Solutions: