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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are developing a model to predict customer churn using Snowflake ML. After training a Gradient Boosting model, you want to understand the relationship between 'number_of_products' and the churn probability. You generate a partial dependence plot (PDP) for 'number_of_products'. The PDP shows a steep increase in churn probability as 'number_of_products' increases from 1 to 3, followed by a plateau. Which of the following statements are the MOST accurate interpretations of this PDP? Assume the dataset is balanced and has undergone proper preprocessing.

A) The model is perfectly calibrated, and the PDP accurately represents the true causal effect of 'number_of_products' on churn.
B) Customers who purchase more than 3 products are less likely to churn, suggesting higher engagement or satisfaction.
C) There might be a confounding variable correlated with both 'number_of_products' and churn, leading to a spurious relationship in the PDP.
D) Increasing the number of products purchased by all customers will definitively reduce overall churn.
E) The PDP indicates a high degree of interaction between 'number_of_products' and other features in the model, making the interpretation unreliable.

2. You have a Snowpark DataFrame named 'product_reviews' containing customer reviews for different products. The DataFrame includes columns like 'product_id' , 'review_text' , and 'rating'. You want to perform sentiment analysis on the 'review_text' to identify the overall sentiment towards each product. You decide to use Snowpark for Python to create a user-defined function (UDF) that utilizes a pre-trained sentiment analysis model hosted externally. You need to ensure secure access to this model and efficient execution. Which of the following represents the BEST approach, considering security and performance?

A) Create a Snowpark Pandas UDF that calls the external sentiment analysis API. Use Snowflake secrets management to store the API key and retrieve it within the UDF.
B) Create an external function in Snowflake that calls a serverless function. Configure the API gateway in front of the serverless function to enforce authentication via Mutual TLS (mTLS) using Snowflake-managed certificates.
C) Create a Java UDF that utilizes a library to call the sentiment analysis API. Pass the API key as a parameter to the UDF each time it is called.
D) Create an inline Python UDF that directly calls the external sentiment analysis API with hardcoded API keys within the UDF code.
E) Create an external function in Snowflake that calls a serverless function (e.g., AWS Lambda, Azure Function) that performs the sentiment analysis. Use Snowflake's network policies to restrict access to the serverless function and secrets management to handle API keys.

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

4. You are tasked with building a fraud detection model using Snowflake and Snowpark Python. The model needs to identify fraudulent transactions in real-time with high precision, even if it means missing some actual fraud cases. Which combination of optimization metric and model tuning strategy would be most appropriate for this scenario, considering the importance of minimizing false positives (incorrectly flagging legitimate transactions as fraudulent)?

A) Precision, optimized with a threshold adjustment to minimize false positives.
B) F 1-Score, optimized to balance precision and recall equally.
C) Log Loss, optimized with a grid search focusing on hyperparameters that improve overall accuracy.
D) Recall, optimized with a threshold adjustment to minimize false negatives.
E) AUC-ROC, optimized with a randomized search focusing on hyperparameters related to model complexity.

5. You are tasked with identifying fraudulent transactions in a large financial dataset stored in Snowflake using unsupervised learning. The dataset contains features like transaction amount, merchant ID, location, time, and user ID. You decide to use a combination of clustering and anomaly detection techniques. Which of the following steps and techniques would be MOST effective in achieving this goal while leveraging Snowflake's capabilities and minimizing false positives?

A) Use a Snowflake Python UDF to perform feature selection, apply a combination of K-means clustering and anomaly detection techniques like Isolation Forest or Local Outlier Factor (LOF), and then score each transaction based on its likelihood of being fraudulent. Tune parameters and use a hold-out validation set to minimize false positives, using a Snowpark DataFrame to retrieve the data.
B) Use only the 'transaction amount' feature and perform histogram-based anomaly detection in Snowflake SQL by identifying values outside of the common ranges, disregarding other potentially relevant information.
C) Perform K-means clustering on the entire dataset using all available features, then flag any transaction that falls outside of any cluster as fraudulent. Ignore any feature selection or engineering to simplify the process.
D) Implement an Isolation Forest algorithm directly in SQL using complex JOINs and window functions to identify anomalies based on transaction volume and velocity.
E) Apply Principal Component Analysis (PCA) for dimensionality reduction, then use DBSCAN clustering to identify dense regions of normal transactions and flag any transaction that is not within a dense region as potentially fraudulent. After, review the anomalous data points.

Solutions: