Snowflake DSA-C03 Exam Questions : SnowPro Advanced: Data Scientist Certification Exam

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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. A retail company is using Snowflake to store transaction data'. They want to create a derived feature called 'customer _ recency' to represent the number of days since a customer's last purchase. The transactions table 'TRANSACTIONS has columns 'customer_id' (INT) and 'transaction_date' (DATE). Which of the following SQL queries is the MOST efficient and scalable way to derive this feature as a materialized view in Snowflake?

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

2. You are tasked with deploying a time series forecasting model within Snowflake using Snowpark Python. The model requires significant pre-processing and feature engineering steps that are computationally intensive. These steps include calculating rolling statistics, handling missing values with imputation, and applying various transformations. You aim to optimize the execution time of these pre- processing steps within the Snowpark environment. Which of the following techniques can significantly improve the performance of your data preparation pipeline?

A) Convert the Snowpark DataFrame to a Pandas DataFrame using and perform all pre-processing operations using Pandas functions before loading the processed data back to Snowflake.
B) Write the feature engineering logic directly in SQL and create a view. Use the Snowpark DataFrame API to query the view, avoiding Python code execution within Snowpark.
C) Utilize Snowpark's vectorized UDFs and DataFrame operations to leverage Snowflake's distributed computing capabilities.
D) Ensure that all data used is small enough to fit within the memory of the client machine running the Snowpark Python script, thus removing the need for distributed computing.
E) Force single-threaded execution by setting to avoid overhead associated with parallel processing.

3. You are building a fraud detection model for an e-commerce platform. One of the features is 'purchase_amount', which ranges from $1 to $10,000. The data has a skewed distribution with many small purchases and a few very large ones. You need to normalize this feature for your model, which uses gradient descent. Which normalization technique(s) would be most suitable in Snowflake, considering the data characteristics and the need to handle potential future outliers?

A) Robust scaling using interquartile range (IQR) in a stored procedure with Python:

B) Z-score standardization using the following SQL:

C) Power Transformer (e.g., Yeo-Johnson) implemented with Snowpark Python:

D) Min-Max scaling using the following SQL:

E) Unit Vector normalization (L2 Normalization) using SQL:

4. You have a structured dataset in Snowflake containing customer information and purchase history. You aim to build a multi-class classification model to predict customer churn, categorizing customers into 'Low Risk', 'Medium Risk', and 'High Risk' of churning. After training the model, you want to evaluate its performance. Which of the following metrics and evaluation techniques, when used together, provide the MOST comprehensive understanding of the model's performance across all churn risk categories, especially when dealing with potential class imbalance?

A) Log Loss (Cross-Entropy Loss), Gini Coefficient, and Kolmogorov-Smirnov (KS) statistic.
B) Only Overall Accuracy and a confusion Matrix.
C) Area Under the ROC Curve (AUC-ROC) for each class (one-vs-rest approach), Precision-Recall Curve for each class, and Cumulative Accuracy Profile (CAP) curve.
D) Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (Coefficient of Determination).
E) Overall Accuracy, Precision, Recall, F I-Score for each class, and Confusion Matrix.

5. You are using a Snowflake Notebook to analyze customer churn for a telecommunications company. You have a dataset with millions of rows and want to perform feature engineering using a combination of SQL transformations and Python code. Your goal is to create a new feature called 'average_monthly call_duration' which calculates the average call duration for each customer over the last 3 months. You are using the Snowpark DataFrame API within your notebook. Given the following code snippet to start with:

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

Solutions: