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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

Question 1

You are building a machine learning model using Snowflake data to predict customer churn. Your dataset includes a 'CUSTOMER TYPE column with the following possible values: 'New', 'Returning', and 'VIP'. You need to perform one-hot encoding on this column. Which of the following Snowflake SQL queries correctly implements one-hot encoding for the 'CUSTOMER TYPE column, creating separate binary columns for each customer type ('IS NEW', 'IS RETURNING', 'IS VIP')?

A. Option E
B. Option A
C. Option D
D. Option C
E. Option B

Question 2

You are analyzing customer churn for a telecommunications company. You have a Snowflake table called 'CUSTOMER ACTIVITY with columns 'CUSTOMER ID', 'CALL DURATION_SUM' (total call duration in minutes), 'DATA USAGE GB' (total data usage in GB), 'CONTRACT LENGTH MONTHS', and 'CHURNED' (boolean indicating whether the customer churned). You want to understand the relationship between these features and churn. Specifically, you want to visualize the distribution of 'CALL DURATION SUM' for churned and non-churned customers. Which of the following visualizations, combined with appropriate Snowflake SQL to prepare the data, would BEST illustrate the relationship between 'CALL DURATION SUM' and 'CHURNED'?

A. A box plot with 'CHURNED on the x-axis and "CALL DURATION SUM' on the y-axis, generated using an external visualization tool connected to Snowflake, after preparing the data using a CTE (Common Table Expression) in Snowflake to categorize customers by churn status.
B. A histogram of 'CALL DURATION SUM" for churned customers and a separate histogram of "CALL DURATION SUM' for non-churned customers, generated using an external visualization tool connected to Snowflake, after preparing the data using a CTE (Common Table Expression) in Snowflake to categorize customers by churn status.
C. A scatter plot with on the x-axis and 'CHURNED' (0 or 1) on the y-axis, generated directly from the table using an external visualization tool connected to Snowflake.
D. A line chart plotting the average 'CALL DURATION SUM' over time, ignoring the 'CHURNED' status.
E. A pie chart showing the percentage of churned and non-churned customers, with no consideration of 'CALL DURATION SUM'

Question 3

A data science team is tasked with deploying a pre-built anomaly detection model in Snowflake to identify fraudulent transactions. They need to use Snowflake ML functions and a Snowflake Native App (that houses the model) to achieve this. The Snowflake Native App is installed and available. The transaction data is stored in a table called 'TRANSACTIONS. Which of the following steps are essential to successfully deploy and use this pre-built model within a User Defined Function (UDF) for real-time scoring, assuming the app provides a function named 'ANOMALY SCORE?

A. Create a UDF that calls the 'ANOMALY _ SCORE function provided by the Snowflake Native App, passing the relevant transaction features as arguments.
B. Ensure the 'TRANSACTIONS' table is shared with the Snowflake Native App's container so the model can directly access the data.
C. Train the pre-built anomaly detection model using Snowflake's ML functions (e.g., 'CREATE MODELS) with the 'TRANSACTIONS' data before creating the UDE
D. Grant the USAGE privilege on the Snowflake Native App to the role executing the UDF. This ensures the UDF can access the app's functionality.
E. Create an external function in API Integration instead of UDF.

Question 4

You have a Snowflake table 'PRODUCT_PRICES' with columns 'PRODUCT_ID' (INTEGER) and 'PRICE' (VARCHAR). The 'PRICE' column sometimes contains values like '10.50 USD', '20.00 EUR', or 'Invalid Price'. You need to convert the 'PRICE column to a NUMERIC(10,2) data type, removing currency symbols and handling invalid price strings by replacing them with NULL. Considering both data preparation and feature engineering, which combination of Snowpark SQL and Python code snippets achieves this accurately and efficiently, preparing the data for further analysis?

A. Option E
B. Option A
C. Option D
D. Option C
E. Option B

Question 5

You are using Snowpark Python to process a large dataset of website user activity logs stored in a Snowflake table named 'WEB ACTIVITY'. The table contains columns such as 'USER ID', 'TIMESTAMP', 'PAGE URL', 'BROWSER', and 'IP ADDRESS'. You need to remove irrelevant data to improve model performance. Which of the following actions, either alone or in combination, would be the MOST effective for removing irrelevant data for a model predicting user conversion rates, and which Snowpark Python code snippets demonstrate these actions? Assume that conversion depends on page interaction and a model will only leverage session id and session duration.

A. Option E
B. Option A
C. Option D
D. Option C
E. Option B

Solutions: