QUESTION NO: 1
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 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?
Correct Answer: B
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QUESTION NO: 2
A data scientist is building a model in Snowflake to predict customer churn. They have a dataset with features like 'age', 'monthly_spend', 'contract_length', and 'complaints'. The target variable is 'churned' (0 or 1). They decide to use a Logistic Regression model. However, initial performance is poor. Which of the following actions could MOST effectively improve the model's performance, considering best practices for Supervised Learning in a Snowflake environment focused on scalable and robust deployment?
A data scientist is building a model in Snowflake to predict customer churn. They have a dataset with features like 'age', 'monthly_spend', 'contract_length', and 'complaints'. The target variable is 'churned' (0 or 1). They decide to use a Logistic Regression model. However, initial performance is poor. Which of the following actions could MOST effectively improve the model's performance, considering best practices for Supervised Learning in a Snowflake environment focused on scalable and robust deployment?
Correct Answer: C
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QUESTION NO: 3
You are building a churn prediction model for a telecommunications company using Snowflake and Snowpark ML. You have trained a Gradient Boosting Machine (GBM) model and want to understand the feature importance to identify key drivers of churn. You've used SHAP (SHapley Additive exPlanations) values to explain individual predictions. Given a customer with a high churn risk, you observe that the 'monthly_charges' feature has a significantly large negative SHAP value for that specific prediction. Which of the following statements best interprets this observation in the context of feature impact?
You are building a churn prediction model for a telecommunications company using Snowflake and Snowpark ML. You have trained a Gradient Boosting Machine (GBM) model and want to understand the feature importance to identify key drivers of churn. You've used SHAP (SHapley Additive exPlanations) values to explain individual predictions. Given a customer with a high churn risk, you observe that the 'monthly_charges' feature has a significantly large negative SHAP value for that specific prediction. Which of the following statements best interprets this observation in the context of feature impact?
Correct Answer: B
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QUESTION NO: 4
You are developing a machine learning model using scikit-learn within Visual Studio Code (VS Code) and connecting directly to Snowflake to access a large dataset. You need to authenticate to Snowflake using Key Pair Authentication, but want to avoid storing the private key directly within your VS Code project or environment variables for security reasons. Which of the following approaches offers the MOST secure way to manage and access the private key for Snowflake authentication from VS Code?
You are developing a machine learning model using scikit-learn within Visual Studio Code (VS Code) and connecting directly to Snowflake to access a large dataset. You need to authenticate to Snowflake using Key Pair Authentication, but want to avoid storing the private key directly within your VS Code project or environment variables for security reasons. Which of the following approaches offers the MOST secure way to manage and access the private key for Snowflake authentication from VS Code?
Correct Answer: E
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QUESTION NO: 5
You're working on a fraud detection system for an e-commerce platform. You have a table 'TRANSACTIONS with a 'TRANSACTION AMOUNT column. You want to bin the transaction amounts into several risk categories ('Low', 'Medium', 'High', 'Very High') using explicit boundaries. You want the bins to be inclusive of the lower boundary and exclusive of the upper boundary (e.g., [0, 100), [100, 500), etc.). Which of the following SQL statements using the 'WIDTH BUCKET function correctly bins the transaction amounts into these categories, assuming these boundaries: 0, 100, 500, 1000, and infinity, and assigns appropriate labels?
You're working on a fraud detection system for an e-commerce platform. You have a table 'TRANSACTIONS with a 'TRANSACTION AMOUNT column. You want to bin the transaction amounts into several risk categories ('Low', 'Medium', 'High', 'Very High') using explicit boundaries. You want the bins to be inclusive of the lower boundary and exclusive of the upper boundary (e.g., [0, 100), [100, 500), etc.). Which of the following SQL statements using the 'WIDTH BUCKET function correctly bins the transaction amounts into these categories, assuming these boundaries: 0, 100, 500, 1000, and infinity, and assigns appropriate labels?
Correct Answer: E
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QUESTION NO: 6
You are performing exploratory data analysis on a large sales dataset in Snowflake using Snowpark. The dataset contains columns such as 'order_id', , and 'profit'. You want to identify the top 5 most profitable products for each month. You have already created a Snowpark DataFrame named 'sales_df. Which of the following Snowpark operations, when combined correctly, will efficiently achieve this?
You are performing exploratory data analysis on a large sales dataset in Snowflake using Snowpark. The dataset contains columns such as 'order_id', , and 'profit'. You want to identify the top 5 most profitable products for each month. You have already created a Snowpark DataFrame named 'sales_df. Which of the following Snowpark operations, when combined correctly, will efficiently achieve this?
Correct Answer: E
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QUESTION NO: 7
A data scientist is tasked with creating features for a machine learning model predicting customer churn. They have access to the following data in a Snowflake table named 'CUSTOMER ID, 'DATE, 'ACTIVITY _ TYPE' (e.g., 'login', 'purchase', 'support_ticket'), and 'ACTIVITY VALUE (e.g., amount spent, duration of login). Which of the following feature engineering strategies, leveraging Snowflake's capabilities, could be useful for predicting customer churn? (Select all that apply)
A data scientist is tasked with creating features for a machine learning model predicting customer churn. They have access to the following data in a Snowflake table named 'CUSTOMER ID, 'DATE, 'ACTIVITY _ TYPE' (e.g., 'login', 'purchase', 'support_ticket'), and 'ACTIVITY VALUE (e.g., amount spent, duration of login). Which of the following feature engineering strategies, leveraging Snowflake's capabilities, could be useful for predicting customer churn? (Select all that apply)
Correct Answer: A,B,C,D
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QUESTION NO: 8
You are using Snowpark Python to process a large dataset of website user activity logs stored in a Snowflake table named 'WEB ACTIVITY'. The table contains columns such as 'USER ID', 'TIMESTAMP', 'PAGE URL', 'BROWSER', and 'IP ADDRESS'. You need to remove irrelevant data to improve model performance. Which of the following actions, either alone or in combination, would be the MOST effective for removing irrelevant data for a model predicting user conversion rates, and which Snowpark Python code snippets demonstrate these actions? Assume that conversion depends on page interaction and a model will only leverage session id and session duration.
You are using Snowpark Python to process a large dataset of website user activity logs stored in a Snowflake table named 'WEB ACTIVITY'. The table contains columns such as 'USER ID', 'TIMESTAMP', 'PAGE URL', 'BROWSER', and 'IP ADDRESS'. You need to remove irrelevant data to improve model performance. Which of the following actions, either alone or in combination, would be the MOST effective for removing irrelevant data for a model predicting user conversion rates, and which Snowpark Python code snippets demonstrate these actions? Assume that conversion depends on page interaction and a model will only leverage session id and session duration.
Correct Answer: A
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QUESTION NO: 9
You are a data scientist working with a large dataset of customer transactions stored in Snowflake. You need to identify potential fraud using statistical summaries. Which of the following approaches would be MOST effective in identifying unusual spending patterns, considering the need for scalability and performance within Snowflake?
You are a data scientist working with a large dataset of customer transactions stored in Snowflake. You need to identify potential fraud using statistical summaries. Which of the following approaches would be MOST effective in identifying unusual spending patterns, considering the need for scalability and performance within Snowflake?
Correct Answer: C,E
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QUESTION NO: 10
A telecom company, 'ConnectPlus', observes that the individual call durations of its customers are heavily skewed towards shorter calls, following an exponential distribution. A data science team aims to analyze call patterns and requires to perform hypothesis testing on the average call duration. Which of the following statements regarding the applicability of the Central Limit Theorem (CLT) in this scenario are correct if the sample size is sufficiently large?
A telecom company, 'ConnectPlus', observes that the individual call durations of its customers are heavily skewed towards shorter calls, following an exponential distribution. A data science team aims to analyze call patterns and requires to perform hypothesis testing on the average call duration. Which of the following statements regarding the applicability of the Central Limit Theorem (CLT) in this scenario are correct if the sample size is sufficiently large?
Correct Answer: C,D
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QUESTION NO: 11
You are using Snowpark for Python to perform feature engineering on a large dataset stored in a Snowflake table named 'transactions'. You need to create a new feature called 'transaction_size category' based on the 'transaction_amount' column. The categories are defined as follows: Small (amount < 10), Medium (10 <= amount < 100), and Large (amount 100). You want to optimize performance by leveraging Snowflake's parallel processing capabilities. Which of the following Snowpark for Python code snippets is the MOST efficient and Pythonic way to achieve this?
You are using Snowpark for Python to perform feature engineering on a large dataset stored in a Snowflake table named 'transactions'. You need to create a new feature called 'transaction_size category' based on the 'transaction_amount' column. The categories are defined as follows: Small (amount < 10), Medium (10 <= amount < 100), and Large (amount 100). You want to optimize performance by leveraging Snowflake's parallel processing capabilities. Which of the following Snowpark for Python code snippets is the MOST efficient and Pythonic way to achieve this?
Correct Answer: B
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QUESTION NO: 12
Consider the following Python UDF intended to train a simple linear regression model using scikit-learn within Snowflake. The UDF takes feature columns and a target column as input and returns the model's coefficients and intercept as a JSON string. You are encountering an error during the CREATE OR REPLACE FUNCTION statement because of the incorrect deployment of the package during runtime. What would be the right way to fix this deployment and execute your model?
Consider the following Python UDF intended to train a simple linear regression model using scikit-learn within Snowflake. The UDF takes feature columns and a target column as input and returns the model's coefficients and intercept as a JSON string. You are encountering an error during the CREATE OR REPLACE FUNCTION statement because of the incorrect deployment of the package during runtime. What would be the right way to fix this deployment and execute your model?
Correct Answer: E
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QUESTION NO: 13
You are tasked with building a Python stored procedure in Snowflake to train a Gradient Boosting Machine (GBM) model using XGBoost.
The procedure takes a sample of data from a large table, trains the model, and stores the model in a Snowflake stage. During testing, you notice that the procedure sometimes exceeds the memory limits imposed by Snowflake, causing it to fail. Which of the following techniques can you implement within the Python stored procedure to minimize memory consumption during model training?
You are tasked with building a Python stored procedure in Snowflake to train a Gradient Boosting Machine (GBM) model using XGBoost.
The procedure takes a sample of data from a large table, trains the model, and stores the model in a Snowflake stage. During testing, you notice that the procedure sometimes exceeds the memory limits imposed by Snowflake, causing it to fail. Which of the following techniques can you implement within the Python stored procedure to minimize memory consumption during model training?
Correct Answer: A
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