最も有効な DSA-C03 試験参考書

You are building a fraud detection model in Snowflake using Snowpark Python. You want to evaluate the model's performance, particularly focusing on identifying instances of fraud (minority class). Which combination of metrics provides the most comprehensive assessment for this imbalanced classification problem within the Snowflake environment, considering the need to minimize both false positives (legitimate transactions flagged as fraudulent) and false negatives (fraudulent transactions missed)?

• A. Accuracy and ROC AUC.
• B. ROC AUC and Recall.
• C. Accuracy and Recall.
• D. Precision, Recall, and Fl-score.
• E. Precision and Fl-score.

You are using Snowpark to build a collaborative filtering model for product recommendations. You have a table 'USER_ITEM INTERACTIONS with columns 'USER ID', 'ITEM ID', and 'INTERACTION TYPE'. You want to create a sparse matrix representation of this data using Snowpark, suitable for input into a matrix factorization algorithm. Which of the following code snippets best achieves this while efficiently handling large datasets within Snowflake?

• A.
  
• B.
  
• C.
  
• D.
  
• E.
  

You are tasked with building a machine learning pipeline in Snowpark Python to predict customer lifetime value (CLTV). You need to access and manipulate data residing in multiple Snowflake tables and views, including customer demographics, purchase history, and website activity. To improve code readability and maintainability, you decide to encapsulate data access and transformation logic within a Snowpark Stored Procedure. Given the following Python code snippet representing a simplified version of your stored procedure:

• A. The 'snowflake.snowpark.context.get_active_session()' function retrieves the active Snowpark session object, enabling interaction with the Snowflake database from within the stored procedure.
• B. The replace=True, packages=['snowflake-snowpark-python', 'pandas', decorator registers the Python function as a Snowpark Stored Procedure, allowing it to be called from SQL.
• C. The 'session.sql('SELECT FROM PURCHASE line executes a SQL query against the Snowflake database and returns the results as a list of Row objects.
• D. The 'session.write_pandas(df, table_name='CLTV PREDICTIONS', auto_create_table=Truey function writes the Pandas DataFrame 'df containing the CLTV predictions directly to a new Snowflake table named , automatically creating the table if it does not exist.
• E. The 'session.table('CUSTOMER DEMOGRAPHICS')' method creates a local Pandas DataFrame containing a copy of the data from the 'CUSTOMER DEMOGRAPHICS' table.

You're building a model to predict whether a user will click on an ad (binary classification: click or no-click) using Snowflake. The data is structured and includes features like user demographics, ad characteristics, and past user interactions. You've trained a logistic regression model using SNOWFLAKE.ML and are now evaluating its performance. You notice that while the overall accuracy is high (around 95%), the model performs poorly at predicting clicks (low recall for the 'click' class). Which of the following steps could you take to diagnose the issue and improve the model's ability to predict clicks, and how would you implement them using Snowflake SQL? SELECT ALL THAT APPLY.

• A. Generate a confusion matrix using SQL to visualize the model's performance across both classes. Example SQL:
  
• B. Reduce the amount of training data to avoid overfitting. Overfitting is known to produce low recall for the 'click' class.
• C. Calculate precision, recall, F I-score, and AUC for the 'click' class using SQL queries to get a more detailed understanding of the model's performance on the minority class. Example:
  
• D. Increase the complexity of the model by switching to a non-linear algorithm like Random Forest or Gradient Boosting without performing hyperparameter tuning, as more complex models always perform better.
• E. Implement feature engineering by creating interaction terms or polynomial features from existing features using SQL, to capture potentially non-linear relationships between features and the target variable. Example:
  

You are developing a model to predict house prices based on structured data including size, number of bedrooms, location, and age. You have built a linear regression model within Snowflake. During the evaluation, you observe that the residuals exhibit heteroscedasticity. Which of the following actions is the LEAST appropriate to address heteroscedasticity in this scenario, considering you want to implement the solution primarily using Snowflake's built-in features and capabilities?

• A. Apply a logarithmic transformation to the target variable ('SALES_PRICE) using the 'LOG' function within Snowflake before training the linear regression model.
• B. Transform independent variables using Box-Cox transformation and include in Snowflake Linear Regression Model Training
• C. Use robust standard errors in the linear regression analysis, even though Snowflake doesn't directly support calculating them. You decide to export model coefficients to an external statistics package (e.g., Python with Statsmodels) to compute robust standard errors and then bring insights back to Snowflake.
• D. Implement Weighted Least Squares (WLS) regression by calculating weights inversely proportional to the variance of the residuals for each data point. This involves creating a UDF to calculate weights and modifying the linear regression model fitting process. (Assume direct modification of the fitting process is possible within Snowflake).
• E. Include interaction terms between the independent variables in your linear regression model.