최신Snowflake SnowPro Advanced: Data Scientist Certification - DSA-C03무료샘플문제
문제1
You have trained a fraud detection model using scikit-learn and want to deploy it in Snowflake using the Snowflake Model Registry. You've registered the model as 'fraud _ model' in the registry. You need to create a Snowflake user-defined function (UDF) that loads and executes the model. Which of the following code snippets correctly creates the UDF, assuming the model is a serialized pickle file stored in a stage named 'model_stage'?
You have trained a fraud detection model using scikit-learn and want to deploy it in Snowflake using the Snowflake Model Registry. You've registered the model as 'fraud _ model' in the registry. You need to create a Snowflake user-defined function (UDF) that loads and executes the model. Which of the following code snippets correctly creates the UDF, assuming the model is a serialized pickle file stored in a stage named 'model_stage'?
정답: D
설명: (ITDumpsKR 회원만 볼 수 있음)
문제2
You are building a customer support chatbot using Snowflake Cortex and a large language model (LLM). You want to use prompt engineering to improve the chatbot's ability to answer complex questions about product features. You have a table PRODUCT DETAILS with columns 'feature_name', Which of the following prompts, when used with the COMPLETE function in Snowflake Cortex, is MOST likely to yield the best results for answering user questions about specific product features, assuming you are aiming for concise and accurate responses focused solely on providing the requested feature description and avoiding extraneous chatbot-like conversation?
You are building a customer support chatbot using Snowflake Cortex and a large language model (LLM). You want to use prompt engineering to improve the chatbot's ability to answer complex questions about product features. You have a table PRODUCT DETAILS with columns 'feature_name', Which of the following prompts, when used with the COMPLETE function in Snowflake Cortex, is MOST likely to yield the best results for answering user questions about specific product features, assuming you are aiming for concise and accurate responses focused solely on providing the requested feature description and avoiding extraneous chatbot-like conversation?
정답: B
설명: (ITDumpsKR 회원만 볼 수 있음)
문제3
You have developed a customer churn prediction model using Python and deployed it as a Snowflake UDE You are monitoring its performance and notice a significant drop in accuracy over time. To address this, you need to implement automated model retraining with regular validation. Which of the following steps and validation techniques are MOST critical for ensuring the retrained model is effective and avoids overfitting to recent data? (Select THREE)
You have developed a customer churn prediction model using Python and deployed it as a Snowflake UDE You are monitoring its performance and notice a significant drop in accuracy over time. To address this, you need to implement automated model retraining with regular validation. Which of the following steps and validation techniques are MOST critical for ensuring the retrained model is effective and avoids overfitting to recent data? (Select THREE)
정답: A,B,D
설명: (ITDumpsKR 회원만 볼 수 있음)
문제4
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?
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?
정답: C,D
설명: (ITDumpsKR 회원만 볼 수 있음)
문제5
You are building a machine learning model using Snowpark for Python and have a feature column called 'TRANSACTION AMOUNT' in your 'transaction_df DataFrame. This column contains some missing values ('NULL). Your model is sensitive to missing data'. You want to impute the missing values using the median "TRANSACTION AMOUNT, but ONLY for specific customer segments (e.g., customers with a 'CUSTOMER TIER of 'Gold' or 'Platinum'). For other customer tiers, you want to impute with the mean. Which of the following Snowpark Python code snippets BEST achieves this selective imputation?
You are building a machine learning model using Snowpark for Python and have a feature column called 'TRANSACTION AMOUNT' in your 'transaction_df DataFrame. This column contains some missing values ('NULL). Your model is sensitive to missing data'. You want to impute the missing values using the median "TRANSACTION AMOUNT, but ONLY for specific customer segments (e.g., customers with a 'CUSTOMER TIER of 'Gold' or 'Platinum'). For other customer tiers, you want to impute with the mean. Which of the following Snowpark Python code snippets BEST achieves this selective imputation?
정답: B
설명: (ITDumpsKR 회원만 볼 수 있음)
문제6
You are deploying a machine learning model to Snowflake using a Python UDF. The model predicts customer churn based on a set of features. You need to handle missing values in the input data'. Which of the following methods is the MOST efficient and robust way to handle missing values within the UDF, assuming performance is critical and you don't want to modify the underlying data tables?
You are deploying a machine learning model to Snowflake using a Python UDF. The model predicts customer churn based on a set of features. You need to handle missing values in the input data'. Which of the following methods is the MOST efficient and robust way to handle missing values within the UDF, assuming performance is critical and you don't want to modify the underlying data tables?
정답: B
설명: (ITDumpsKR 회원만 볼 수 있음)
문제7
You are working with a large dataset of transaction data in Snowflake to identify fraudulent transactions. The dataset contains millions of rows and includes features like transaction amount, location, time, and user ID. You want to use Snowpark and SQL to identify potential outliers in the 'transaction amount' feature. Given the potential for skewed data and varying transaction volumes across different locations, which of the following data profiling and feature engineering techniques would be the MOST effective at identifying outlier transaction amounts while considering the data distribution and location-specific variations?
You are working with a large dataset of transaction data in Snowflake to identify fraudulent transactions. The dataset contains millions of rows and includes features like transaction amount, location, time, and user ID. You want to use Snowpark and SQL to identify potential outliers in the 'transaction amount' feature. Given the potential for skewed data and varying transaction volumes across different locations, which of the following data profiling and feature engineering techniques would be the MOST effective at identifying outlier transaction amounts while considering the data distribution and location-specific variations?
정답: C,E
설명: (ITDumpsKR 회원만 볼 수 있음)
문제8
You've trained a machine learning model using Scikit-learn and saved it as 'model.joblib'. You need to deploy this model to Snowflake. Which sequence of commands will correctly stage the model and create a Snowflake external function to use it for inference, assuming you already have a Snowflake stage named 'model_stage'?
You've trained a machine learning model using Scikit-learn and saved it as 'model.joblib'. You need to deploy this model to Snowflake. Which sequence of commands will correctly stage the model and create a Snowflake external function to use it for inference, assuming you already have a Snowflake stage named 'model_stage'?
정답: D
설명: (ITDumpsKR 회원만 볼 수 있음)
문제9
You are deploying a large language model (LLM) to Snowflake using a user-defined function (UDF). The LLM's model file, '11m model.pt', is quite large (5GB). You've staged the file to Which of the following strategies should you employ to ensure successful deployment and efficient inference within Snowflake? Select all that apply.
You are deploying a large language model (LLM) to Snowflake using a user-defined function (UDF). The LLM's model file, '11m model.pt', is quite large (5GB). You've staged the file to Which of the following strategies should you employ to ensure successful deployment and efficient inference within Snowflake? Select all that apply.
정답: A,B,D
설명: (ITDumpsKR 회원만 볼 수 있음)
문제10
Consider the following Snowflake SQL query used to calculate the RMSE for a regression model's predictions, where 'actual_value' is the actual value and 'predicted value' is the model's prediction. However, you notice that the RMSE calculation is incorrect due to an error in the query. Identify the error in the query and provide the corrected query. The table name is 'sales_predictions'.
Which of the following options represents the corrected query that accurately calculates the RMSE?
Consider the following Snowflake SQL query used to calculate the RMSE for a regression model's predictions, where 'actual_value' is the actual value and 'predicted value' is the model's prediction. However, you notice that the RMSE calculation is incorrect due to an error in the query. Identify the error in the query and provide the corrected query. The table name is 'sales_predictions'.
Which of the following options represents the corrected query that accurately calculates the RMSE?
정답: E
설명: (ITDumpsKR 회원만 볼 수 있음)
문제11
You are validating a time series forecasting model for daily sales using Snowflake and Snowpark. The residuals plot shows a clear sinusoidal pattern. Which of the following actions should you consider to improve your model? (Select all that apply)
You are validating a time series forecasting model for daily sales using Snowflake and Snowpark. The residuals plot shows a clear sinusoidal pattern. Which of the following actions should you consider to improve your model? (Select all that apply)
정답: A,C
설명: (ITDumpsKR 회원만 볼 수 있음)
문제12
You are building a predictive model for customer churn using linear regression in Snowflake. You have identified several features, including 'CUSTOMER AGE', 'MONTHLY SPEND', and 'NUM CALLS'. After performing an initial linear regression, you suspect that the relationship between 'CUSTOMER AGE and churn is not linear and that older customers might churn at a different rate than younger customers. You want to introduce a polynomial feature of "CUSTOMER AGE (specifically, 'CUSTOMER AGE SQUARED') to your regression model within Snowflake SQL before further analysis with python and Snowpark. How can you BEST create this new feature in a robust and maintainable way directly within Snowflake?
You are building a predictive model for customer churn using linear regression in Snowflake. You have identified several features, including 'CUSTOMER AGE', 'MONTHLY SPEND', and 'NUM CALLS'. After performing an initial linear regression, you suspect that the relationship between 'CUSTOMER AGE and churn is not linear and that older customers might churn at a different rate than younger customers. You want to introduce a polynomial feature of "CUSTOMER AGE (specifically, 'CUSTOMER AGE SQUARED') to your regression model within Snowflake SQL before further analysis with python and Snowpark. How can you BEST create this new feature in a robust and maintainable way directly within Snowflake?
정답: B
설명: (ITDumpsKR 회원만 볼 수 있음)
문제13
You're building a fraud detection model and want to determine if the average transaction amount for fraudulent transactions is significantly higher than the average transaction amount for legitimate transactions. You have two tables in Snowflake:
'FRAUDULENT TRANSACTIONS and 'LEGITIMATE TRANSACTIONS, both with a 'TRANSACTION AMOUNT column. You believe that FRAUDULENT TRANSACTIONS contains fewer than 30 transactions. You don't know the population standard deviations. What are the proper steps to conduct the hypothesis test, and what is the correct hypothesis statement?
You're building a fraud detection model and want to determine if the average transaction amount for fraudulent transactions is significantly higher than the average transaction amount for legitimate transactions. You have two tables in Snowflake:
'FRAUDULENT TRANSACTIONS and 'LEGITIMATE TRANSACTIONS, both with a 'TRANSACTION AMOUNT column. You believe that FRAUDULENT TRANSACTIONS contains fewer than 30 transactions. You don't know the population standard deviations. What are the proper steps to conduct the hypothesis test, and what is the correct hypothesis statement?
정답: B
설명: (ITDumpsKR 회원만 볼 수 있음)
문제14
A financial institution is analyzing transaction data in Snowflake to detect fraudulent activity. They have a 'Transaction_Amount' column. They want to binarize this feature, creating a new 'ls_High_Value' column. Transactions with amounts greater than $1000 should be marked as 1 (High Value), and all other transactions (including NULLs) should be marked as 0. Which of the following SQL statements would be the MOST efficient and correct way to achieve this in Snowflake?
A financial institution is analyzing transaction data in Snowflake to detect fraudulent activity. They have a 'Transaction_Amount' column. They want to binarize this feature, creating a new 'ls_High_Value' column. Transactions with amounts greater than $1000 should be marked as 1 (High Value), and all other transactions (including NULLs) should be marked as 0. Which of the following SQL statements would be the MOST efficient and correct way to achieve this in Snowflake?
정답: E
설명: (ITDumpsKR 회원만 볼 수 있음)