A healthcare provider has a Snowflake table 'MEDICAL RECORDS containing patient notes stored as unstructured text in a column called 'NOTE TEXT. They want to identify different patient groups based on the topics discussed in these notes. They aim to use a combination of unsupervised and supervised learning. Which of the following represents a robust workflow to achieve this goal?

• A. Perform topic modeling (e.g., LDA) directly on the 'NOTE_TEXT column using a Python UDF in Snowflake. Manually label a subset of the resulting topics. Then, train a supervised classifier (e.g., Naive Bayes) to predict the identified topics for new patient notes.
• B. Export all 'NOTE TEXT data to an extemal system, use an existing NLP pipeline for topic modeling and manual labeling, then create a Snowflake UDF that replicates this entire pipeline internally.
• C. MultiOutputClassifier wrapped around a Logistic Regression model) within Snowflake (using Snowpark), using the original 'NOTE TEXT as input features (TF-IDF or word embeddings) and the manually assigned topic labels as target variables. Use the trained model to classify the remaining patient notes into relevant patient groups.
• D. Use a Snowflake external function to call a pre-trained topic modeling model (e.g., BERTopic) hosted on Google Cloud A1 Platform. Assign topic probabilities to each patient note. Then, perform K-Means clustering on the topic probabilities to identify patient segments. No manual labeling is performed.
• E. Perform topic modeling on a sample of the 'NOTE TEXT data using a Snowflake Python UDF. Manually review the top documents for each identified topic, and assign labels describing the patient group represented by each topic. Train a supervised multi-label classification model (e.g., using scikit-learn's

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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.

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

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You are using a Snowflake Notebook to build a churn prediction model. You have engineered several features, and now you want to visualize the relationship between two key features: and , segmented by the target variable 'churned' (boolean). Your goal is to create an interactive scatter plot that allows you to explore the data points and identify any potential patterns.
Which of the following approaches is most appropriate and efficient for creating this visualization within a Snowflake Notebook?

• A. Use the Snowflake Connector for Python to fetch the data, then leverage a Python visualization library like Plotly or Bokeh to generate an interactive plot within the notebook.
• B. Use the 'snowflake-connector-python' to pull the data and use 'seaborn' to create static plots.
• C. Write a stored procedure in Snowflake that generates the visualization data in a specific format (e.g., JSON) and then use a JavaScript library within the notebook to render the visualization.
• D. Leverage Snowflake's native support for Streamlit within the notebook to create an interactive application. Query the data directly from Snowflake within the Streamlit app and use Streamlit's plotting capabilities for visualization.
• E. Create a static scatter plot using Matplotlib directly within the Snowflake Notebook by converting the data to a Pandas DataFrame. This involves pulling all relevant data into the notebook's environment before plotting.

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You are working with a dataset in Snowflake containing customer reviews stored in a 'REVIEWS' table. The 'SENTIMENT SCORE column contains continuous values ranging from -1 (negative) to 1 (positive). You need to create a new column, 'SENTIMENT CATEGORY, based on the following rules: 'Negative': 'SENTIMENT SCORE < -0.5 'Neutral': -0.5 'SENTIMENT SCORE 0.5 'Positive': 'SENTIMENT SCORE > 0.5 You also want to binarize this 'SENTIMENT CATEGORY column into three separate columns: 'IS NEGATIVE, 'IS NEUTRAL', and 'IS POSITIVE. Which of the following SQL statements correctly implements both the categorization and subsequent binarization?

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

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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?

• A. Fine-tune the LLM on the entire knowledge base. Train a custom LLM model specifically on the knowledge base data.
• B. Partition your database by different subject matter and then query the specific partitions for your information.
• C. Use Retrieval-Augmented Generation (RAG). Generate vector embeddings for the knowledge base entries, perform a similarity search to find the most relevant entries for each customer question, and include those entries in the prompt.
• D. Use Snowflake Cortex's 'COMPLETE function without any external knowledge base. Rely solely on the LLM's pre-trained knowledge.
• E. Directly prompt the LLM with the entire knowledge base content for each customer question. Concatenate all knowledge base entries into a single string and include it in the prompt.

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