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Snowflake DSA-C03 Exam Syllabus Topics:

Section	Objectives
Topic 1: Machine Learning with Snowpark	- Using Snowpark for Python-based ML workflows - Model training and evaluation workflows
Topic 2: Data Engineering for Machine Learning	- SQL-based feature engineering - Data pipelines using Snowflake
Topic 3: Model Deployment and Operationalization	- Model deployment in Snowflake ecosystem - Monitoring and lifecycle management
Topic 4: Data Science Fundamentals in Snowflake	- Data preprocessing and transformation in Snowflake - Applied statistics and data exploration
Topic 5: Advanced Analytics and Optimization	- Scalable analytics design patterns - Performance optimization of data queries

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

Question #1
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. 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.
B. Create a UDF that calls the 'ANOMALY _ SCORE function provided by the Snowflake Native App, passing the relevant transaction features as arguments.
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. Ensure the 'TRANSACTIONS' table is shared with the Snowflake Native App's container so the model can directly access the data.
E. Create an external function in API Integration instead of UDF.

Question #2
You are tasked with building a data science pipeline in Snowflake to predict customer churn. You have trained a scikit-learn model and want to deploy it using a Python UDTF for real-time predictions. The model expects a specific feature vector format. You've defined a UDTF named 'PREDICT CHURN' that loads the model and makes predictions. However, when you call the UDTF with data from a table, you encounter inconsistent prediction results across different rows, even when the input features seem identical. Which of the following are the most likely reasons for this behavior and how would you address them?

A. The UDTF is not partitioning data correctly. Ensure the UDTF utilizes the 'PARTITION BY clause in your SQL query based on a relevant dimension (e.g., 'customer_id') to prevent state inconsistencies across partitions. This will isolate the impact of any statefulness within the function
B. The issue is related to the immutability of the Snowflake execution environment for UDTFs. To resolve this, cache the loaded model instance within the UDTF's constructor and reuse it for subsequent predictions. Using a global variable is also acceptable.
C. The input feature data types in the table do not match the expected data types by the scikit-learn model. Cast the input columns to the correct data types (e.g., FLOAT, INT) before passing them to the UDTF. Use explicit casting functions like 'TO DOUBLE and INTEGER in your SQL query.
D. There may be an error in model, where the 'predict method is producing different ouputs for the same inputs. Retraining the model will resolve the issue.
E. The scikit-learn model was not properly serialized and deserialized within the UDTF. Ensure the model is saved using 'joblib' or 'pickle' with appropriate settings for cross-platform compatibility and loaded correctly within the UDTF's 'process' method. Verify serialization/deserialization by testing it independently from Snowflake first.

Question #3
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 line chart plotting the average 'CALL DURATION SUM' over time, ignoring the 'CHURNED' status.
B. A pie chart showing the percentage of churned and non-churned customers, with no consideration of 'CALL DURATION SUM'
C. 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.
D. 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.
E. 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.

Question #4
You are working with a Snowflake table named 'CUSTOMER DATA' containing customer information, including a 'PHONE NUMBER' column. Due to data entry errors, some phone numbers are stored as NULL, while others are present but in various inconsistent formats (e.g., with or without hyphens, parentheses, or country codes). You want to standardize the 'PHONE NUMBER column and replace missing values using Snowpark for Python. You have already created a Snowpark DataFrame called 'customer df representing the 'CUSTOMER DATA' table. Which of the following approaches, used in combination, would be MOST efficient and reliable for both cleaning the existing data and handling future data ingestion, given the need for scalability?

A. Create a Snowflake Stored Procedure in SQL that uses regular expressions and 'CASE statements to format the "PHONE_NUMBER column and replace NULL values. Call this stored procedure from a Snowpark Python script.
B. Use a UDF (User-Defined Function) written in Python that formats the phone numbers based on a regular expression and applies it to the DataFrame using For NULL values, replace them with a default value of 'UNKNOWN'.
C. Leverage Snowflake's data masking policies to mask any invalid phone number and create a view that replaces NULL values with 'UNKNOWN'. This approach doesn't correct existing data but hides the issue.
D. Create a Snowflake Pipe with a COPY INTO statement and a transformation that uses a SQL function within the COPY INTO statement to format the phone numbers and replace NULL values during data loading. Also, implement a Python UDF for correcting already existing data.
E. Use a series of and methods on the Snowpark DataFrame to handle NULL values and different phone number formats directly within the DataFrame operations.

Question #5
You are designing a feature engineering pipeline using Snowpark Feature Store for a fraud detection model. You have a transaction table in Snowflake. One crucial feature is the 'average_transaction_amount_last_7_days' for each customer. You want to implement this feature using Snowpark Python and materialize it in the Feature Store. You have the following Snowpark DataFrame 'transactions_df containing 'customer_id' and 'transaction_amount'. Which of the following code snippets correctly defines and registers this feature in the Snowpark Feature Store, ensuring efficient computation and storage?

A.

B.

C.

D.

E.