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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are tasked with presenting a business case to stakeholders demonstrating the value of a new machine learning model that predicts customer churn. The model has been trained on data within Snowflake, and you have various metrics such as accuracy, precision, recall, and F I-score. You also have feature importance scores generated using a SHAP (SHapley Additive exPlanations) explainer. Which of the following visualization strategies, when combined, would MOST effectively communicate the model's performance and impact to a non-technical audience, while also providing sufficient detail for technical stakeholders?

A) A ROC curve (Receiver Operating Characteristic) showing the trade-off between true positive rate and false positive rate, paired with a detailed table of all feature importance scores generated by the SHAP explainer. Present statistical summaries, such as mean and standard deviation, of the top 5 feature values, grouped by predicted churn probability.
B) A simple bar chart showing the overall accuracy score of the model alongside a table detailing the precision, recall, and F I-score. Include a word cloud of the most important features from the SHAP values.
C) A confusion matrix visualizing the true positives, true negatives, false positives, and false negatives, along with a summary plot of the SHAP values showing the impact of each feature on the model's prediction for a representative sample of customers. A line chart showing cumulative churn rate across different customer segments.
D) A distribution plot (e.g., histogram or KDE) of the predicted churn probabilities, segmented by actual churn status (churned vs. not churned), combined with a SHAP force plot visualizing the feature contributions for a single, randomly selected customer who churned. Add a section on potential cost savings from churn reduction.
E) A scatter plot showing the relationship between two key features identified by SHAP, colored by the model's churn prediction, and a table summarizing the model's performance metrics (accuracy, precision, recall, F I-score). Additionally, include a waterfall plot for a specific customer, illustrating how each feature contributes to the final prediction.

2. A data scientist is analyzing sales data in Snowflake to identify seasonal trends. The 'SALES TABLE' contains columns 'SALE DATE' (DATE) and 'SALE _ AMOUNT' (NUMBER). They want to calculate the average daily sales amount for each month and year in the dataset. Which of the following SQL queries will correctly achieve this, while also handling potential NULL values in 'SALE AMOUNT?

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

3. You are building a real-time fraud detection system using Snowpark ML and Dynamic Tables. The raw transaction data arrives continuously in a Snowflake stream. You need to create a data science pipeline that continuously transforms the data, trains a model, and scores new transactions in near real-time. Which combination of Snowflake features provides the BEST solution for achieving low latency and high throughput for this fraud detection system? Select all that apply:

A) Dynamic Tables to continuously transform the raw transaction data into features required by the model, with 'WAREHOUSE SIZE set to 'X-LARGE to ensure sufficient compute resources.
B) Snowpark ML User-Defined Functions (UDFs) to apply the fraud detection model to incoming transactions, executed using Snowflake's vectorized engine for optimal performance.
C) Snowpipe with Auto-Ingest to load the raw transaction data into a staging table before processing it with Dynamic Tables.
D) Scheduled Snowflake tasks to retrain the model every hour based on the most recent transaction data.
E) Snowflake Tasks with a 'WHEN SYSTEM$STREAM HAS clause to incrementally process new transactions from the stream and update feature tables.

4. 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) Precision, optimized with a threshold adjustment to minimize false positives.
B) Log Loss, optimized with a grid search focusing on hyperparameters that improve overall accuracy.
C) F 1-Score, optimized to balance precision and recall equally.
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.

5. A data scientist is building a linear regression model in Snowflake to predict customer churn based on structured data stored in a table named 'CUSTOMER DATA'. The table includes features like 'CUSTOMER D', 'AGE, 'TENURE MONTHS', 'NUM PRODUCTS', and 'AVG MONTHLY SPEND'. The target variable is 'CHURNED' (1 for churned, 0 for active). After building the model, the data scientist wants to evaluate its performance using Mean Squared Error (MSE) on a held-out test set. Which of the following SQL queries, executed within Snowflake's stored procedure framework, is the MOST efficient and accurate way to calculate the MSE for the linear regression model predictions against the actual 'CHURNED values in the 'CUSTOMER DATA TEST table, assuming the linear regression model is named 'churn _ model' and the predicted values are generated by the MODEL APPLY() function?

A)

B)

C)

D)

E)

Solutions: