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Snowflake SnowPro Advanced Data Scientist

The Snowflake SnowPro Advanced Data Scientist validates the ability to apply data science and machine learning techniques within the Snowflake environment. it focuses on using Snowflake's features to prepare data for modeling and integrate with external ML tools for advanced analytics. Professionals with the symbol SNOW_SADS are experts in leveraging Snowflake to drive intelligent business decisions through data.



---------- Question 1
A data scientist needs to interpret the predictions of a complex machine learning model to understand which features are most influential in its decisions for specific instances. Which model explainability technique is designed to show the marginal effect of a feature on the predicted outcome, averaged over all other features?
  1. Confusion Matrix
  2. Area Under the Curve (AUC)
  3. Partial Dependence Plots (PDP)
  4. Root Mean Squared Error (RMSE)

---------- Question 2
A data scientist has deployed a classification model to predict customer churn. After deployment, they observe that the model frequently predicts non-churn for customers who actually churn, leading to missed intervention opportunities. Which metric primarily indicates this issue and should be prioritized for improvement?
  1. Accuracy
  2. Precision
  3. F1-Score
  4. Recall

---------- Question 3
During the development of a predictive model in Snowflake, a data scientist finds that the model's performance is highly sensitive to parameters like learning rate and regularization strength. Which technique is essential to systematically search for the optimal combination of these parameters to maximize model performance?
  1. Cross-validation
  2. Down-sampling
  3. Hyperparameter tuning
  4. Feature scaling

---------- Question 4
Your team wants to implement a semantic search capability on customer feedback text stored in Snowflake. Which Snowflake Cortex function is most relevant for converting textual data into numerical vectors to measure semantic similarity?
  1. COMPLETE
  2. SENTIMENT
  3. EMBED_TEXT
  4. SUMMARIZE

---------- Question 5
A data science team wants to use Snowflake Cortex to analyze customer reviews stored in a Snowflake table to understand the overall sentiment towards their products. Which Snowflake Cortex capability is most appropriate for this task?
  1. Vector Embedding for similarity search.
  2. Prompt Engineering for custom model interactions.
  3. Task-specific models for sentiment analysis.
  4. Fine-tuning a base LLM for domain-specific language.

---------- Question 6
A data scientist wants to automate a data transformation step in their Snowpark ML pipeline that involves complex, custom logic. This logic needs to be applied to a DataFrame column by column in a scalable manner, directly within Snowflake. Which Snowpark construct is best suited for this operation?
  1. External function
  2. Python User-Defined Function UDF
  3. SQL Stored Procedure
  4. Dynamic Table

---------- Question 7
A deployed machine learning model for predicting customer churn starts showing degraded performance in production, even though the input data schema has not changed. What two key concepts related to model effectiveness and retraining should the data scientist investigate to diagnose and address this issue?
  1. Model explainability and feature impact
  2. Hyperparameter tuning and cross-validation
  3. Data drift and model decay
  4. A/B testing and ensemble methods

---------- Question 8
When performing exploratory data analysis (EDA) on a Snowflake table containing sales transactions, a data scientist wants to calculate the moving average of sales over the past seven days for each product. Which Snowflake native statistical function and SQL construct should be utilized?
  1. AVG function with GROUP BY clause
  2. SUM function with a subquery
  3. Window function AVG OVER PARTITION BY ORDER BY ROWS BETWEEN
  4. COUNT function with a HAVING clause

---------- Question 9
To effectively manage and track changes to different versions of a machine learning model, including associated training data and performance metrics, throughout its lifecycle within Snowflake, which feature provides robust support for metadata tagging and versioning?
  1. Snowflake Dynamic Tables
  2. Python User-Defined Functions (UDFs)
  3. Snowflake Model Registry
  4. Snowflake External Stages

---------- Question 10
After deploying several versions of a machine learning model in Snowflake, a data scientist needs a centralized system to log model artifacts, track performance metrics across versions, and retrieve specific model versions for rollback or A/B testing. Which Snowflake feature is designed to provide these capabilities?
  1. Snowflake Stages
  2. Dynamic Tables
  3. Snowflake Model Registry
  4. Streamlit in Snowflake

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