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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are building a real-time fraud detection system using Snowpark ML and Dynamic Tables. The raw transaction data arrives continuously in a Snowflake stream. You need to create a data science pipeline that continuously transforms the data, trains a model, and scores new transactions in near real-time. Which combination of Snowflake features provides the BEST solution for achieving low latency and high throughput for this fraud detection system? Select all that apply:

A) Snowpark ML User-Defined Functions (UDFs) to apply the fraud detection model to incoming transactions, executed using Snowflake's vectorized engine for optimal performance.
B) Scheduled Snowflake tasks to retrain the model every hour based on the most recent transaction data.
C) Snowflake Tasks with a 'WHEN SYSTEM$STREAM HAS clause to incrementally process new transactions from the stream and update feature tables.
D) Dynamic Tables to continuously transform the raw transaction data into features required by the model, with 'WAREHOUSE SIZE set to 'X-LARGE to ensure sufficient compute resources.
E) Snowpipe with Auto-Ingest to load the raw transaction data into a staging table before processing it with Dynamic Tables.

2. You are developing a fraud detection model in Snowflake. You've identified that transaction amounts and transaction frequency are key features. You observe that the transaction amounts are heavily right-skewed and the transaction frequencies have outliers. Furthermore, the model needs to be robust against seasonal variations in transaction frequency. Which of the following feature engineering steps, when applied in sequence, would be MOST appropriate to handle these data characteristics effectively?

A) 1. Apply min-max scaling to the transaction amounts. 2. Remove outliers in transaction frequency using the Interquartile Range (IQR) method. 3. Calculate the cumulative sum of transaction frequencies.
B) 1. Apply a Box-Cox transformation to the transaction amounts. 2. Apply a quantile-based transformation (e.g., using NTILE) to the transaction frequencies to map them to a uniform distribution. 3. Calculate the difference between the current transaction frequency and the average transaction frequency for that day of the week over the past year.
C) 1. Apply a logarithmic transformation to the transaction amounts. 2. Apply a Winsorization technique to the transaction frequencies to handle outliers. 3. Calculate a rolling average of transaction frequency over a 7-day window.
D) 1. Apply a square root transformation to the transaction amounts. 2. Standardize the transaction frequencies using Z-score normalization. 3. Create dummy variables for the day of the week.
E) 1. Apply a logarithmic transformation to the transaction amounts. 2. Replace outliers in transaction frequency with the mean value. 3. Create lag features of transaction frequency for the previous 7 days.

3. A marketing analyst is building a propensity model to predict customer response to a new product launch. The dataset contains a 'City' column with a large number of unique city names. Applying one-hot encoding to this feature would result in a very high-dimensional dataset, potentially leading to the curse of dimensionality. To mitigate this, the analyst decides to combine Label Encoding followed by binarization techniques. Which of the following statements are TRUE regarding the benefits and challenges of this combined approach in Snowflake compared to simply label encoding?

A) Label encoding introduces an arbitrary ordinal relationship between the cities, which may not be appropriate. Binarization alone cannot remove this artifact.
B) While label encoding itself adds an ordinal relationship, applying binarization techniques like binary encoding (converting the label to binary representation and splitting into multiple columns) after label encoding will remove the arbitrary ordinal relationship.
C) Label encoding followed by binarization will reduce the memory required to store the 'City' feature compared to one-hot encoding, and Snowflake's columnar storage optimizes storage for integer data types used in label encoding.
D) Binarizing a label encoded column using a simple threshold (e.g., creating a 'high_city_id' flag) addresses the curse of dimensionality by reducing the number of features to one, but it loses significant information about the individual cities.
E) Binarization following label encoding may enhance model performance if a specific split based on a defined threshold is meaningful for the target variable (e.g., distinguishing between cities above/below a certain average income level related to marketing success).

4. You are developing a churn prediction model using Snowpark Python and Scikit-learn. After initial model training, you observe significant overfitting. Which of the following hyperparameter tuning strategies and code snippets, when implemented within a Snowflake Python UDF, would be MOST effective to address overfitting in a Ridge Regression model and how can you implement a reproducible model with minimal code?

A) Option D
B) Option B
C) Option C
D) Option E
E) Option A

5. You have deployed a fraud detection model in Snowflake and are monitoring its performance. The initial AUC was 0.92. After a month, you observe the AUC has dropped to 0.78. You suspect data drift. Which of the following steps should you take FIRST to investigate and address this performance degradation, focusing on efficient resource utilization within Snowflake?

A) Increase the complexity of the existing model architecture by adding more layers to the neural network to improve its adaptability.
B) Delete the existing model and deploy a pre-trained, generic fraud detection model obtained from a public repository.
C) Immediately retrain the model using the entire dataset available, scheduling a Snowpark Python UDF to perform the training.
D) Analyze the distributions of key features in the current production data compared to the training data using Snowflake SQL queries and visualization tools. Specifically compare the distributions of features such as transaction amount and time of day. Then, if drift is confirmed, retrain using updated data.
E) Deploy a new model version with a higher classification threshold to compensate for the increased false positives.

Solutions: