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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. A data science team is evaluating different methods for summarizing lengthy customer support tickets using Snowflake Cortex. The goal is to generate concise summaries that capture the key issues and resolutions. Which of the following approaches is/are appropriate for achieving this goal within Snowflake, considering the need for efficiency, cost-effectiveness, and scalability? (Select all that apply)

A) Creating a custom summarization model using a transformer-based architecture like BART or T5, training it on a large dataset of support tickets and summaries within Snowflake using Snowpark ML, and then deploying this custom model for generating summaries via a UDF.
B) Employing a SQL-based approach using string manipulation functions and keyword extraction techniques to identify important sentences and concatenate them to form a summary.
C) Using the 'SNOWFLAKE.ML.PREDICT' function with a summarization task-specific model provided by Snowflake Cortex, passing the full ticket text as input to generate a summary.
D) Calling the Snowflake Cortex 'COMPLETE' endpoint with a detailed prompt that instructs the model to summarize the support ticket, explicitly specifying the desired summary length and format.
E) Developing a Python UDF that leverages a pre-trained summarization model from a library like 'transformers' and deploying it in Snowflake. Managing the model loading and inference within the UDF.

2. You are developing a model to predict equipment failure in a factory using sensor data stored in Snowflake. The data is partitioned by 'EQUIPMENT ID' and 'TIMESTAMP. After initial model training and cross-validation using the following code snippet:

You observe significant performance variations across different equipment groups when evaluating on out-of-sample data'. Which of the following strategies could you employ to address this issue within the Snowflake environment to improve the model's generalization ability across all equipment?

A) Implement cross-validation at the partition level by splitting 'TRAINING_DATX into train and test sets before creating the model, and then using the 'FIT' command to train on the train set and 'PREDICT to evaluate on the test set, repeating for each partition.
B) Retrain the model with additional feature engineering to create interaction terms between 'EQUIPMENT_ID' and other relevant sensor features to capture equipment-specific patterns. For instance, you can one hot encode and add to model and include in 'INPUT DATA'.
C) Create seperate models per equipment ID. For each equipment ID, split data into training and testing data. For each equipment ID, use 'SYSTEM$OPTIMIZE MODEL' to perform hyper parameter search individually. Train and Deploy the model at equipement ID Level.
D) Increase the overall size of the "TRAINING_DATR to include more historical data for all equipment, assuming this will balance the representation of each EQUIPMENT ID'
E) Implement a hyperparameter search using 'SYSTEM$OPTIMIZE_MODEL' with a wider range of parameters for each 'EQUIPMENT_ID individually, creating a separate model for each 'EQUIPMENT ID.

3. You have trained a complex Random Forest model in Snowflake to predict loan default risk. You wish to understand the individual and combined effects of 'credit_score' and 'debt_to_income_ratio' on the predicted probability of default. Which approach is MOST suitable for visualizing and interpreting these relationships?

A) Examine the model's overall accuracy (e.g., AUC) and assume the relationships are well-represented.
B) Calculate feature importance using SNOWFLAKE.ML.FEATURE IMPORTANCE and focus on the features with the highest scores.
C) Generate individual Partial Dependence Plots (PDPs) for 'credit_score' and 'debt_to_income_ratio'.
D) Create a two-way Partial Dependence Plot (PDP) showing the interaction between 'credit_score' and 'debt_to_income_ratio'.
E) Fit a simpler linear model (e.g., Logistic Regression) to the data and interpret its coefficients.

4. 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) 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.
D) 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.
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.

5. A data scientist is developing a fraud detection model using Snowpark ML on Snowflake. They have a feature engineering pipeline implemented as a Snowpark DataFrame transformation. The pipeline includes several complex UDFs. The data scientist observes that the pipeline execution is slow. What are the most effective techniques to optimize the feature engineering pipeline's performance in Snowpark?

A) Disable Snowpark's lazy evaluation by executing on the DataFrame after each transformation.
B) Rewrite Python UDFs as vectorized Python UDFs using the 'pandas' API within Snowpark to leverage batch processing.
C) Cache intermediate DataFrames using or 'persist()' to avoid recomputation of common transformations.
D) Reduce the size of the input DataFrame by sampling the data.
E) Replace Python UDFs with Snowflake SQL UDFs where possible, as SQL UDFs often offer better performance due to Snowflake's optimization capabilities.

Solutions: