Purchase Snowflake : DSA-C03 Exam Materials and then pass exam easily

You are tasked with building a machine learning model in Python using data stored in Snowflake. You need to efficiently load a large table (100GB+) into a Pandas DataFrame for model training, minimizing memory footprint and network transfer time. You are using the Snowflake Connector for Python. Which of the following approaches would be MOST efficient for loading the data, considering potential memory limitations on your client machine and the need for data transformations during the load process?

• A. Use 'snowsql' to unload the table to a local CSV file, then load the CSV file into a Pandas DataFrame.
• B. Load the entire table into a Pandas DataFrame using with a simple 'SELECT FROM my_table' query and then perform data transformations in Pandas.
• C. Create a Snowflake view with the necessary transformations, and then load the view into a Pandas DataFrame using 'pd.read_sql()'.
• D. Use the 'COPY INTO' command to unload the table to an Amazon S3 bucket and then use bot03 in your python script to fetch data from s3 and load into pandas dataframe.
• E. Utilize the 'execute_stream' method of the Snowflake cursor to fetch data in chunks, apply transformations in each chunk, and append to a larger DataFrame or process iteratively without creating a large in-memory DataFrame.

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You are building a model to predict loan defaults using a dataset stored in Snowflake. After training your model and calculating residuals, you create a scatter plot of the residuals against the predicted values. The plot shows a cone-shaped pattern, with residuals spreading out more as the predicted values increase. Which of the following SQL queries, run within a Snowpark Python session, could be used to address the underlying issue indicated by this residual pattern, assuming the predicted values are stored in a column named and the residuals in a column named 'loan_default_residuar in a Snowflake table named 'loan_predictionds'?

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

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You are tasked with predicting sales (SALES AMOUNT') for a retail company using linear regression in Snowflake. The dataset includes features like 'ADVERTISING SPEND', 'PROMOTIONS', 'SEASONALITY INDEX', and 'COMPETITOR PRICE'. After training a linear regression model named 'sales model', you observe that the model performs poorly on new data, indicating potential issues with multicollinearity or overfitting. Which of the following strategies, applied directly within Snowflake, would be MOST effective in addressing these issues and improving the model's generalization performance? Choose ALL that apply.

• A. Increase the size of the training dataset significantly by querying data from external sources.
• B. Decrease the 'MAX_ITERATIONS' parameter in the 'CREATE MODEL' statement to prevent the model from overfitting to the training data.
• C. Manually remove highly correlated features (e.g., if 'ADVERTISING SPEND and 'PROMOTIONS' have a correlation coefficient above 0.8) based on a correlation matrix calculated using 'CORR function and feature selection techniques.
• D. Perform feature scaling (e.g., standardization or min-max scaling) on the input features before training the model, using Snowflake's built-in functions or user-defined functions (UDFs) for scaling.
• E. Apply Ridge Regression by adding an L2 regularization term during model training. This can be achieved by setting the 'REGULARIZATION' parameter of the 'CREATE MODEL' statement to 'L2'.

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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.

• A. Use the 'PUT' command with to compress the model file before staging it. Snowflake will automatically decompress it during UDF execution.
• B. Split the large model file into smaller chunks and stage each chunk separately. Reassemble the model within the UDF code before inference.
• C. Increase the warehouse size to XLARGE or larger to provide sufficient memory for loading the large model into the UDF environment.
• D. Use the 'IMPORTS' clause in the UDF definition to reference Ensure the UDF code loads the model lazily (i.e., only when it's first needed) to minimize startup time and memory usage.
• E. Leverage Snowflake's Snowpark Container Services to deploy the LLM as a separate containerized application and expose it via a Snowpark API. Then call that endpoint from snowflake.

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You are using Snowpark for Python to build a feature engineering pipeline for a machine learning model that predicts customer churn. The data is stored in a Snowflake table called 'CUSTOMER DATA' , and you want to create new features based on time-series data within the table. You need to calculate the 'Recency' feature (days since the last transaction) and 'Frequency' feature (number of transactions in the last 3 months). Considering performance and best practices, which Snowpark approach would you choose?

• A. Write custom Python code in a Snowpark UDF to retrieve each transaction for a customer and calculate recency and frequency directly in Python without pandas.
• B. Fetch the entire 'CUSTOMER DATA table into a Pandas DataFrame using , then use Pandas' time-series functions to calculate 'Recency' and 'Frequency'. After feature engineering, load the Pandas DataFrame back into Snowflake.
• C. Write a stored procedure in SQL that calculates 'Recency' and 'Frequency' using SQL window functions, and then call this stored procedure from your Snowpark Python code.
• D. Use Snowpark DataFrame API to perform window functions within Snowflake to calculate 'Recency' and 'Frequency' directly, leveraging Snowflake's processing power without transferring data to the client.
• E. Create a Python UDF using Pandas to calculate 'Recency' and 'Frequency'. Apply this UDF to the 'CUSTOMER DATA' table through Snowpark, processing the data row by row.

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