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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are performing exploratory data analysis on a large sales dataset in Snowflake using Snowpark. The dataset contains columns such as 'order_id', , and 'profit'. You want to identify the top 5 most profitable products for each month. You have already created a Snowpark DataFrame named 'sales_df. Which of the following Snowpark operations, when combined correctly, will efficiently achieve this?

A) Group by 'product_id', aggregate 'sum(profity, then use partitioned by ordered by 'sum(profit) DESC' within a UDF.
B) Use 'rank()' partitioned by ordered by 'sum(profit) DESC' , after grouping by and 'product_id' , and aggregating 'sum(profity.
C) Group by and 'product_id' , aggregate 'sum(profit)' , then use partitioned by ordered by 'sum(profit) DESC'.
D) Use 'ntile(5)' partitioned by ordered by 'sum(profit) DESC' after grouping by and 'product_id', and aggregating 'sum(profit)'.
E) First, create a temporary table with aggregated monthly profit for each product using SQL. Then, use Snowpark to read the temporary table and apply a window function partitioned by ordered by 'sum(profit) DESC'.

2. A financial institution wants to predict fraudulent transactions on credit card data stored in Snowflake. The dataset includes features like transaction amount, merchant ID, location, time of day, and user profile information. The target variable is 'is_fraudulent' (0 or 1). You have trained several binary classification models (Logistic Regression, Random Forest, and Gradient Boosting) using scikit-learn and persisted them using a Snowflake external function for inference. To optimize for both performance (inference speed) and accuracy, which of the following steps should you consider before deploying your model for real-time scoring using the external function? SELECT ALL THAT APPLY.

A) Replace the trained models with a simple rule-based system based solely on transaction amount. If the amount is greater than a threshold, flag it as fraudulent, as this will be faster than calling the external function.
B) Evaluate the models on a representative held-out dataset within Snowflake using SQL queries (e.g., calculating AUC, precision, recall) to choose the model with the best balance of performance and accuracy before deploying it.
C) Implement feature selection techniques (e.g., using feature importance scores from Random Forest or Gradient Boosting) to reduce the number of features passed to the external function, improving inference speed.
D) Increase the batch size of requests sent to the external function to amortize the overhead of invoking the external function itself, even if it increases latency for individual transactions.
E) Normalize or standardize the input features in Snowflake using SQL before passing them to the external function to ensure consistent scaling and potentially improve model performance.

3. You are deploying a pre-trained image classification model stored as a serialized file in an internal stage within Snowflake. You need to create a UDF to load this model and use it for inference on image data stored in a VARIANT column. The model was trained using Python's scikit-learn library and uses OpenCV for image processing. Which of the following code snippets correctly outlines the steps required to create and deploy this UDF? Assume you have already created an internal stage named 'MODEL STAGE and uploaded the model file into it. You also need to create a temporary directory that will be removed after the execution.

A)

B)

C)

D)

E)

4. You are performing exploratory data analysis on a dataset containing customer transaction data in Snowflake. The dataset has a column named 'transaction_amount' and a column named 'customer_segment'. You want to analyze the distribution of transaction amounts for each customer segment using Snowflake's statistical functions. Which of the following approaches would BEST achieve this, providing insights into the central tendency and spread of the data?

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

5. You are tasked with building a model to predict customer churn. You have a table named in Snowflake with the following relevant columns: 'customer_id', 'login_date', , 'orders_placed', , and 'churned' (binary indicator). You want to engineer features that capture customer engagement over time using Snowpark for Python. Which of the following feature engineering steps, applied sequentially, are MOST effective in creating features indicative of churn risk?

A) 1. Calculate the maximum 'page_views' in a single day for each customer. 2. Calculate the total number of days with no 'login_date' for each customer. 3. Create a feature indicating if a customer has ever placed an order. 4. Use a simple boolean for the 'subscription_type' column.
B) 1. Calculate the average 'page_views' per week for each customer over the last 3 months using a window function. 2. Calculate the recency of the last order (days since last order) for each customer. 3. Create a feature indicating the change in average daily page views over the last month compared to the previous month. 4. Create a feature showing standard deviation of page_views per customer over the last 90 days.
C) 1. Calculate the average 'page_views' per day for each customer. 2. Calculate the total number of for each customer. 3. Create a feature indicating whether the customer has a premium subscription ('subscription_type' = 'premium').
D) 1. Calculate the total 'page_views' and 'orders_placed' for each customer without considering time. 2. Use one-hot encoding for the 'subscription_type' column.
E) 1. Calculate the number of days since the customer's last login, and use nulls instead of negative numbers to indicate inactivity. 2. Calculate the rolling 7-day average of 'orders_placed' using a window function, partitioning by 'customer_id' and ordering by 'login_date'. 3. Calculate the slope of a linear regression of page_views' over time for each customer, indicating the trend in engagement using Snowpark ML. 4. Calculate the percentage of weeks the customer logged in. 5. Create a feature showing standard deviation of page_views per customer over the last 90 days.

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