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NEW QUESTION # 222
You are developing a Python stored procedure in Snowflake to train a machine learning model using scikit-learn. The training data resides in a Snowflake table named 'SALES DATA. You need to pass the feature columns (e.g., 'PRICE, 'QUANTITY) and the target column ('REVENUE) dynamically to the stored procedure. Which of the following approaches is the MOST secure and efficient way to achieve this, preventing SQL injection vulnerabilities and ensuring data integrity within the stored procedure?
Answer: A
Explanation:
Passing the column names as a VARIANT array and using parameterized queries is the safest and most efficient approach. This avoids SQL injection vulnerabilities, as the column names are treated as data rather than code. It also allows Snowflake to optimize the query execution plan. Options A and C are vulnerable to SQL injection. Option D doesn't address the core problem of dynamically specifying columns and security. Option E introduces an extra layer of abstraction (the view) but doesn't inherently solve the dynamic column specification or SQL injection risks if the view definition is itself dynamically constructed.
NEW QUESTION # 223
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?
Answer: B
Explanation:
Option B is the most efficient and Pythonic. It leverages Snowpark's built-in 'when' function for conditional logic, which is optimized for execution within Snowflake's distributed processing environment. It avoids the overhead of creating a UDF (Option A) or transferring the data to Pandas (Options C and E), which would negate the benefits of Snowpark. Option D, while functional, involves creating a temporary view and executing SQL, which is less integrated with the Snowpark Python API than using 'when'. Furthermore, Option C and E converts the Snowpark dataframe to pandas dataframe which is a performance bottleneck.
NEW QUESTION # 224
You are tasked with training a machine learning model within Snowflake using a Python UDTF. The UDTF is intended to process incoming sales data, calculate features, and update the model incrementally. The model is a simple linear regression using scikit-learn. Your initial attempt fails with a 'ModuleNotFoundError: No module named 'sklearn" error within the UDTF. You have already confirmed that scikit-learn is available in your Anaconda channel and specified it during session creation. Which of the following actions would MOST directly address this issue and allow the UDTF to successfully import and use scikit-learn?
Answer: D
Explanation:
The 'PACKAGES parameter within the 'CREATE FUNCTION' statement is the MOST direct and reliable way to ensure that specific Python packages are available to your UDTF. Options A, B, and C might address related issues, but directly specifying the package in the function definition is the recommended approach. Option E, although technically feasible, is not a best practice and can lead to dependency management issues. The Snowpark session is automatically created and is not the source of sklearn not being available. The Anaconda environment is a construct that provides the channel information, but the function needs an explict reference to the packages to include within the function body.
NEW QUESTION # 225
You are using Snowflake Cortex to build a customer support chatbot that leverages LLMs to answer customer questions. You have a knowledge base stored in a Snowflake table. The following options describe different methods for using this knowledge base in conjunction with the LLM to generate responses. Which of the following approaches will likely result in the MOST accurate, relevant, and cost-effective responses from the LLM?
Answer: C
Explanation:
RAG (Retrieval-Augmented Generation) is the most effective approach (C). It combines the benefits of LLMs with the ability to incorporate external knowledge. Prompting with the entire knowledge base (A) is inefficient and might exceed context limits. Relying solely on the pre-trained LLM (B) won't leverage your specific knowledge base. Fine-tuning (D) is expensive and requires significant effort and only parititioning (E) won't help.
NEW QUESTION # 226
A data science team is using Snowpark ML to train a classification model. They want to log model metadata (e.g., training parameters, evaluation metrics) and artifacts (e.g., the serialized model file) for reproducibility and model governance purposes. Which of the following approaches is the most appropriate for integrating model logging and artifact management within the Snowpark ML workflow, minimizing operational overhead?
Answer: A
Explanation:
MLflow integration (B) within Snowpark provides a streamlined and integrated solution for model logging and artifact management, minimizing operational overhead by directly tracking experiments, logging parameters/metrics, and storing artifacts within Snowflake stages or external storage. Other options involve more manual work or introduce dependencies on external platforms, increasing complexity and management overhead.
NEW QUESTION # 227
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