Question 1

Following the deployment of a customer churn model in a telecommunications company, monitoring dashboards reveal a steady decline in prediction accuracy over six months, despite the model's prediction UDF remaining unchanged. An investigation confirms the data pipeline is functioning correctly. What is the most probable explanation for this performance degradation?

Accepted Answer

The initial model was overfit to the training data., Data drift, where the statistical properties of the incoming data have changed over time., A bug in the UDF's Python code that only manifests with long-term execution., Insufficient virtual warehouse size for handling prediction requests.

Question 2

A data science team is evaluating several potential projects for a large e-commerce retailer using structured sales and customer data stored in Snowflake. Which of the following business problems are appropriate to frame as a multi-class classification task? (Select Two)

Accepted Answer

Predicting the exact sales revenue for the next fiscal quarter., Categorizing customer support inquiries into predefined topics such as 'Billing', 'Shipping', or 'Product Feedback'., Determining if a customer will make a purchase or not in the next 30 days., Grouping customers into distinct segments based on purchasing behavior without any predefined labels., Classifying products as 'Top Seller', 'Average Seller', or 'Poor Seller' based on historical performance metrics.

Question 3

To maintain reproducibility for a deployed fraud detection model, a data scientist needs to ensure that the exact version used for a specific quarterly report can be easily identified and redeployed. Which Snowflake Model Registry feature is specifically designed to create a stable, human-readable pointer to a model version for this purpose?

Accepted Answer

The model version's unique hash., The `comment` field of the model version., A model alias., The creation timestamp of the model version.

Question 4

A data scientist at a retail company needs to compute the total revenue, average order value, and the number of unique customers for each product category from a `SALES` Snowpark DataFrame. Which Snowpark for Python code snippet accomplishes this MOST efficiently?

Accepted Answer

rev = df_sales.group_by('CATEGORY').agg(F.sum('REVENUE'))
avg_val = df_sales.group_by('CATEGORY').agg(F.avg('ORDER_VALUE'))
uc = df_sales.group_by('CATEGORY').agg(F.count_distinct('CUSTOMER_ID'))
result = rev.join(avg_val, 'CATEGORY').join(uc, 'CATEGORY'), df_sales.sql("SELECT CATEGORY, SUM(REVENUE), AVG(ORDER_VALUE), COUNT(DISTINCT CUSTOMER_ID) FROM identifier('df_sales') GROUP BY CATEGORY"), session.sql("SELECT CATEGORY, SUM(REVENUE), AVG(ORDER_VALUE), COUNT(DISTINCT CUSTOMER_ID) FROM SALES GROUP BY CATEGORY"), df_sales.group_by('CATEGORY').agg(F.sum('REVENUE'), F.avg('ORDER_VALUE'), F.count_distinct('CUSTOMER_ID'))

Question 5

A data scientist begins a new project to predict equipment failure in a manufacturing facility using sensor data. The first step is to perform exploratory data analysis (EDA). Which two of the following are primary objectives of the data visualization and exploration phase in this context? (Select Two)

Accepted Answer

Visualize relationships between different sensor readings and recorded failure events to uncover potential predictive patterns., Build an automated data ingestion pipeline to handle real-time sensor streams for the production model., Characterize the statistical distribution and range of each sensor's data to detect anomalies or data quality issues., Apply dimensionality reduction techniques to the raw sensor data to create a more compact feature set., Partition the dataset into training, validation, and testing sets in preparation for model training.

Question 6

When evaluating two different binary classification models for predicting equipment failure, Model A achieves an AUC of 0.82 and Model B achieves an AUC of 0.91. Both models were trained on the same balanced dataset within a Snowflake Notebook. Which conclusion is the most accurate?

Accepted Answer

Model B has a higher probability of ranking a randomly chosen positive instance higher than a randomly chosen negative instance compared to Model A., Model A is more likely to have a higher precision at any given recall level compared to Model B., Model B is guaranteed to have a lower false positive rate than Model A for the same true positive rate., Model B correctly classifies 9% more instances than Model A.

Question 7

A data scientist has developed a linear regression model to predict the remaining useful life of manufacturing equipment. Upon examining the residuals plot, where residuals are plotted against the predicted values, they observe a distinct U-shaped pattern. What is the most critical issue this pattern indicates, and what is the most appropriate corrective action to take?

Accepted Answer

There are significant outliers in the dataset. The data scientist should remove the data points corresponding to the largest absolute residual values and retrain the model., The model fails to capture a non-linear relationship. The data scientist should introduce polynomial features for the predictors or consider a non-linear model., The data exhibits heteroscedasticity. The data scientist should apply a logarithmic transformation to the target variable to stabilize the variance., The model is suffering from multicollinearity. The data scientist should calculate the Variance Inflation Factor (VIF) for each predictor and remove those with high scores.

Question 8

A data scientist needs to extract specific product names and their corresponding prices from a large table of unstructured customer reviews. The output format must be a consistent JSON object for downstream processing. The initial attempts using a simple directive are producing inconsistent structures. Which prompt engineering technique is MOST suitable for guiding the Snowflake Cortex `COMPLETE` function to produce the desired structured output reliably?

Accepted Answer

Few-shot prompting, by including several examples of input reviews and their corresponding correct JSON output directly in the prompt., Role-playing, by instructing the model to act as a data entry specialist before presenting it with the review text., Zero-shot prompting, by providing a very detailed description of the required JSON schema in the instruction., Chain-of-thought prompting, by asking the model to first explain its logic for identifying a product and price before generating the JSON.

Question 9

A data scientist at a manufacturing firm needs to efficiently load a daily production report, which exists as a 1 million row Pandas DataFrame, into a new Snowflake table. The primary goal is to minimize the load time and client-side processing. Which approach using the Snowflake Connector for Python is the MOST performant for this task?

Accepted Answer

Establish a connection and use the pandas.read_sql() function with the table name., Iterate through the DataFrame rows and execute an INSERT statement for each row., Convert the DataFrame to a CSV file, use the PUT command to upload it to a stage, and then use the COPY INTO command., Use the snowflake.connector.pandas_tools.write_pandas() function.

Question 10

A data science team is tasked with integrating their existing, large-scale Apache Spark MLlib application with Snowflake. The primary objective is to leverage Snowflake as the central data source and sink while minimizing modifications to the existing Spark-native codebase. Which connection method is the MOST appropriate for this requirement?

Accepted Answer

Snowpark for Python, by rewriting the Spark logic as Snowpark DataFrames., The Snowflake Connector for Spark, configuring it as a data source in the existing Spark application., The Snowflake Connector for Python, using it within the Spark application to fetch data into pandas DataFrames., A custom JDBC connection, manually managing data transfer and type mapping.

Question 11

A data scientist is preparing a dataset from a table with over 300 columns for a sales forecasting model. They have identified a Python list containing the names of 45 columns that are irrelevant due to data sparsity and low correlation with the target variable. Which Snowpark for Python approach provides the MOST efficient and idiomatic way to create a new DataFrame that excludes these specified columns?

Accepted Answer

Generate a SQL SELECT statement string in Python that explicitly lists all columns to keep and execute it using session.sql()., Use the Snowpark DataFrame .drop() method, passing the list of column names as an argument., Convert the Snowpark DataFrame to a pandas DataFrame, use the pandas .drop() method, and then convert it back to a Snowpark DataFrame., Iterate through the list of columns to drop, calling the .drop() method for each column individually in a loop.

Question 12

A data scientist has developed two regression models to predict housing prices. Model A has an RMSE of $50,000 and an R-squared of 0.85. Model B has an RMSE of $45,000 and an R-squared of 0.82. Both models were trained on the same dataset. Assuming prediction accuracy is the primary goal, which statement accurately guides the model selection process?

Accepted Answer

Model A is superior because its R-squared value is higher, indicating it explains more variance in the data., Neither model is acceptable because a high R-squared value should always be paired with the lowest possible RMSE., Model B should be chosen because its lower RMSE indicates that, on average, its predictions are closer to the actual housing prices., The models are equivalent, and the choice depends on whether explaining variance or minimizing error is more important.

Question 13

A data scientist is analyzing daily sales data for an e-commerce platform and needs to compute a 7-day moving average of sales for each product category. Which SQL window function clause correctly defines the window frame for this calculation?

Accepted Answer

PARTITION BY category ORDER BY sale_date ROWS BETWEEN 7 PRECEDING AND 1 PRECEDING, PARTITION BY category ORDER BY sale_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW, PARTITION BY category ORDER BY sale_date RANGE BETWEEN INTERVAL '6' DAY PRECEDING AND CURRENT ROW, GROUP BY category ORDER BY sale_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW

Question 14

When evaluating a tree-based model trained in a Snowflake Notebook, a data scientist observes that the default, impurity-based feature importance scores are heavily skewed towards high-cardinality categorical features that have been numerically encoded. What is the primary reason for this behavior?

Accepted Answer

Impurity-based measures are biased and tend to inflate the importance of features with more potential split points., The warehouse size used for training was insufficient, leading to calculation errors., The model is overfitting to the high-cardinality features in the training data., The numerical encoding method is incompatible with tree-based models.

Question 15

To comply with financial regulations, a data science team must be able to prove the data lineage for every model in production. This includes identifying the exact version of the training dataset used for a specific model version. What is the most appropriate way to use metadata tagging to fulfill this requirement?

Accepted Answer

Tag the model with the deployment date and time., Tag the model with the name of the data scientist who trained it., Tag the model with the unique identifier of the training data source, such as a table's time-travel query ID or a stream version., Tag the model with its key performance metric, such as AUC or RMSE.

Question 16

Which of the following are defining characteristics of an unsupervised machine learning problem? (Select Two)

Accepted Answer

The dataset includes a distinct, pre-defined outcome variable for training., The primary goal is to infer patterns or hidden structures from the input data., The model learns from data without explicit labels or a target variable., The model's performance is primarily measured by accuracy, precision, and recall against a test set.

Question 17

A data scientist is analyzing a large e-commerce `ORDERS` table to identify fraudulent activity. Critical indicators of fraud are believed to be user accounts with an unusually high number of orders placed within a very short time frame (e.g., more than 5 orders within any 60-minute window). Which two strategies, using Snowpark and SQL, could be used to effectively identify these critical user accounts? (Select Two)

Accepted Answer

Perform a self-join on the `ORDERS` table, joining on `USER_ID` and a time window condition, then aggregate the results to count orders within the window., Use a recursive CTE in SQL to iterate through each user's orders and check the time difference between every combination of 5 orders., Create a Python UDTF that takes a user's ID and order timestamps, processes them in memory, and returns rows for users who meet the fraud criteria., Use the `LAG` window function to find the time of the 5th previous order for each user and filter where the difference from the current order's time is less than 60 minutes., Use `session.sql()` to execute a query with a `MATCH_RECOGNIZE` clause to define and find the specified pattern of orders within the time window for each user.

Question 18

A data scientist is evaluating a credit default prediction model on a highly imbalanced dataset, where only 2% of customers default. The model achieves an overall accuracy of 98%. However, further analysis reveals a very low recall score for the default class. What does this combination of metrics indicate about the model's performance?

Accepted Answer

The model has high precision, meaning that when it predicts a default, it is almost always correct., The model is primarily predicting the majority class (non-default) and is failing to identify most of the actual default cases., The model is highly effective at identifying customers from both the default and non-default classes., The model's high accuracy is a reliable indicator of its strong predictive power for this business problem.

Question 19

A financial services firm needs to integrate a proprietary, third-party risk scoring model into its Snowflake-based data pipeline. The model is exposed via a secure REST API endpoint hosted in AWS. To ensure maximum security and direct integration for SQL-based workflows, what is the most appropriate and recommended Snowflake feature to use?

Accepted Answer

A Python UDF that uses an external library to make HTTP requests to the API endpoint., A Snowpark procedure that extracts data, calls the external API using Python, and then writes the results back to a Snowflake table., An External Function configured with an API Integration that securely stores credentials and manages the connection to the remote service., Snowpark Container Services to deploy a custom application that acts as a proxy between Snowflake and the external model.

Question 20

Which of the following business objectives are best framed as binary classification problems? (Select Two)

Accepted Answer

Estimating the total sales revenue for the upcoming fiscal year., Predicting whether a customer will churn in the next quarter., Grouping customers into distinct purchasing behavior segments., Identifying if a credit card transaction is fraudulent.

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