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Snowflake DSA-C03 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Preparation and Feature Engineering in Snowflake	25%	- Feature engineering techniques 1. Feature creation and selection 2. Scaling, encoding and normalization 3. Using Snowflake functions for feature processing - Data ingestion and integration 1. Data cleaning and transformation 2. Structured and semi-structured data handling
Topic 2: Machine Learning Model Development and Training	25%	- Model types and selection 1. Unsupervised learning 2. Time-series models 3. Supervised learning - Training and optimization 1. Using Snowflake ML and Snowpark 2. Hyperparameter tuning 3. Model validation and testing
Topic 3: Model Deployment, Monitoring and Governance	15%	- Governance and compliance 1. Security and access control 2. Lineage and audit - Deployment strategies 1. Batch and real-time inference 2. Model serving in Snowflake - Monitoring and maintenance 1. Performance tracking 2. Data drift and model drift detection
Topic 4: Generative AI and LLM Capabilities	15%	- LLM integration in Snowflake 1. Embeddings and vector search 2. Prompt engineering - Generative AI use cases 1. Text generation and summarization 2. Retrieval-augmented generation
Topic 5: Data Science Concepts and Methodologies	20%	- Statistical and mathematical foundations 1. Probability and statistics 2. Evaluation metrics - Data science lifecycle 1. Exploratory data analysis 2. Problem framing and requirements 3. Data collection and acquisition

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

Question #1
You are developing a churn prediction model using Snowpark Python and Scikit-learn. After initial model training, you observe significant overfitting. Which of the following hyperparameter tuning strategies and code snippets, when implemented within a Snowflake Python UDF, would be MOST effective to address overfitting in a Ridge Regression model and how can you implement a reproducible model with minimal code?

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

Question #2
A data scientist needs to calculate the cumulative moving average of sales for each product in a table. The table contains columns: (INT), (DATE), and (NUMBER). The desired output should include the product_id', 'sale_date', and Which of the following Snowflake SQL statements correctly calculates the cumulative moving average for each product using window functions?

A. SELECT product_id, sale_date, daily_sales, AVG(daily_sales) OVER (PARTITION BY product_id) AS cumulative_average FROM sales_by_day;
B. SELECT product_id, sale_date, daily_sales, AVG(daily_sales) OVER (ORDER BY sale_date ASC) AS cumulative_average FROM sales_by_day;
C. SELECT product_id, sale_date, daily_sales, OVER (PARTITION BY product_id ORDER BY sale_date ASC ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS cumulative_average FROM
D. SELECT product_id, sale_date, daily_sales, OVER (PARTITION BY product_id ORDER BY sale_date AS cumulative_average FROM sales_by_day;
E. SELECT product_id, sale_date, daily_sales, OVER (PARTITION BY product_id ORDER BY sale_date ASC) / OVER (PARTITION BY product_id ORDER BY sale_date ASC) AS cumulative_average FROM sales_by_day;

Question #3
You are using Snowpark Feature Store to manage features for your machine learning models. You've created several Feature Groups and now want to consume these features for training a model. To optimize retrieval, you want to use point-in-time correctness. Which of the following actions/configurations are essential to ensure point-in-time correctness when retrieving features using Snowpark Feature Store?

A. When creating Feature Groups, specify a 'timestamp_key' that represents the event timestamp of the data in the source tables.
B. Use the method on the Feature Store client, providing a dataframe containing the 'primary_keyS and the desired for each record.
C. Create an associated Stream on the source tables used for Feature Groups
D. Ensure that all source tables used by the Feature Groups have Change Data Capture (CDC) enabled.
E. Explicitly specify a in the call.

Question #4
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. 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.
B. 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.
C. 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.
D. 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.
E. 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.

Question #5
A data scientist at 'Polaris Analytics' wants to estimate the average transaction value of all online purchases made during the Black Friday sale. Due to the enormous volume of data in Snowflake, they decide to use the Central Limit Theorem (CLT). They randomly sample 1000 transactions daily for 30 days and calculate the sample mean for each day. The sample mean values are stored in a Snowflake table named Which of the following SQL queries, assuming the table has a column of 'FLOAT' type, will provide the best estimate of the population mean and its confidence interval using the CLT?