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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are using Snowpark for Python to build a feature engineering pipeline for a machine learning model that predicts customer churn. The data is stored in a Snowflake table called 'CUSTOMER DATA' , and you want to create new features based on time-series data within the table. You need to calculate the 'Recency' feature (days since the last transaction) and 'Frequency' feature (number of transactions in the last 3 months). Considering performance and best practices, which Snowpark approach would you choose?

A) Create a Python UDF using Pandas to calculate 'Recency' and 'Frequency'. Apply this UDF to the 'CUSTOMER DATA' table through Snowpark, processing the data row by row.
B) Write a stored procedure in SQL that calculates 'Recency' and 'Frequency' using SQL window functions, and then call this stored procedure from your Snowpark Python code.
C) Fetch the entire 'CUSTOMER DATA table into a Pandas DataFrame using , then use Pandas' time-series functions to calculate 'Recency' and 'Frequency'. After feature engineering, load the Pandas DataFrame back into Snowflake.
D) Use Snowpark DataFrame API to perform window functions within Snowflake to calculate 'Recency' and 'Frequency' directly, leveraging Snowflake's processing power without transferring data to the client.
E) Write custom Python code in a Snowpark UDF to retrieve each transaction for a customer and calculate recency and frequency directly in Python without pandas.

2. You are building a customer churn prediction model for a telecommunications company. You have a 'CUSTOMER DATA' table with a 'MONTHLY SPENDING' column that represents the customer's monthly bill amount. You want to binarize this column to create a feature indicating whether a customer is a 'High Spender' or 'Low Spender'. You decide that customers spending more than $75 are 'High Spenders'. Which of the following Snowflake SQL statements is the most efficient and correct way to achieve this, considering performance and readability, while avoiding potential NULL values in the resulting binarized column?

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

3. You are building an image classification model within Snowflake to categorize satellite imagery based on land use types (residential, commercial, industrial, agricultural). The images are stored as binary data in a Snowflake table 'SATELLITE IMAGES. You plan to use a pre-trained convolutional neural network (CNN) from a library like TensorFlow via Snowpark Python UDFs. The model requires images to be resized and normalized before prediction. You have a Python UDF named that takes the image data and model as input and returns the predicted class. What steps are crucial to ensure optimal performance and scalability of the image classification process within Snowflake, considering the volume and velocity of incoming satellite imagery?

A) Utilize Snowflake's external functions to call an image processing service hosted on AWS Lambda or Azure Functions for image resizing and normalization, then pass the processed images to the 'classify_image' UDF.
B) Pre-process the images outside of Snowflake using a separate data pipeline and store the resized and normalized images in a new Snowflake table before running the 'classify_image' UDE
C) Load the entire 'SATELLITE IMAGES table into the UDF for processing, allowing the UDF to handle all image resizing, normalization, and classification tasks sequentially.
D) Use a combination of Snowpark Python UDFs for preprocessing tasks like resizing and normalization, and leverage Snowflake's GPU-accelerated warehouses (if available) to expedite the inference step within the 'classify_image' UDF. Ensure the model weights are efficiently cached.
E) Implement image resizing and normalization directly within the 'classify_image' Python UDF using libraries like OpenCV. Ensure the UDF is vectorized to process images in batches and leverage Snowpark's optimized data transfer capabilities.

4. You are tasked with training a logistic regression model in Snowflake using Snowpark Python to predict customer churn. Your data is stored in a table named 'CUSTOMER DATA' with columns like 'CUSTOMER D', 'FEATURE 1', 'FEATURE 2', 'FEATURE 3', and 'CHURN FLAG' (boolean representing churn). You plan to use stratified k-fold cross-validation to ensure each fold has a representative proportion of churned and non-churned customers. Which of the following code snippets demonstrates the correct way to perform stratified k-fold cross-validation with Snowpark ML? (Assume 'snowpark_session' is a valid Snowpark session object).

A)

B)

C)

D)

E)

5. 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 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.
C) 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.
D) 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.
E) 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.

Solutions: