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Snowflake DSA-C03 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Model Development and Machine Learning	25%–30%	- Model Training 1. Training workflows 2. Hyperparameter tuning 3. Cross validation - Model Evaluation 1. Model explainability 2. Regression metrics 3. Classification metrics
Topic 2: Generative AI and LLM Capabilities	10%–15%	- AI Governance 1. Responsible AI 2. Monitoring AI models - GenAI in Snowflake 1. Prompt engineering 2. Vector embeddings 3. LLM integration
Topic 3: Snowflake Data Science Best Practices	15%–20%	- Security and Governance 1. Role-based access control 2. Data governance - Performance Optimization 1. Warehouse sizing 2. Query optimization
Topic 4: Data Science Concepts	10%–15%	- Data Science Workflow 1. Evaluation metrics 2. Experiment tracking 3. Model lifecycle - Machine Learning Concepts 1. Supervised learning 2. Reinforcement learning 3. Unsupervised learning
Topic 5: Data Preparation and Feature Engineering	25%–30%	- Feature Engineering 1. Feature selection 2. Feature extraction 3. Feature scaling - Data Preparation 1. Handling missing values 2. Data transformation 3. Data cleansing

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. 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)

2. You are developing a fraud detection model in Snowflake using Snowpark Python. You've iterated through multiple versions of the model, each with different feature sets and algorithms. To ensure reproducibility and easy rollback in case of performance degradation, how should you implement model versioning within your Snowflake environment, focusing on the lifecycle step of Deployment & Monitoring?

A) Utilize Snowflake's Time Travel feature to revert to previous versions of the model artifact stored in a Snowflake stage.
B) Implement a custom versioning system using Snowflake stored procedures that track model versions and automatically deploy the latest model by overwriting the existing one. The prior version gets deleted.
C) Store the trained models directly in external cloud storage (e.g., AWS S3, Azure Blob Storage) with explicit versioning enabled on the storage layer, and update Snowflake metadata (e.g., in a table) to point to the current model version. Use a UDF to load the correct model version.
D) Store each model version as a separate Snowflake table, containing serialized model objects and metadata like training date, feature set, and performance metrics. Use views to point to the 'active' version.
E) Only maintain the current model version. If any problems arise, retrain a new model and redeploy it to replace the faulty one.

3. You are tasked with performing data profiling on a large customer dataset in Snowflake to identify potential issues with data quality and discover initial patterns. The dataset contains personally identifiable information (PII). Which of the following Snowpark and SQL techniques would be most appropriate to perform this task while minimizing the risk of exposing sensitive data during the exploratory data analysis phase?

A) Directly query the raw customer data using SQL and Snowpark, computing descriptive statistics like mean, median, and standard deviation for all numeric columns and frequency counts for categorical columns. Store the results in a temporary table for further analysis.
B) Export the entire customer dataset to an external data lake for exploratory analysis using Spark and Python. Apply data masking in Spark before analysis.
C) Create a masked view of the customer data using Snowflake's dynamic data masking features. This view masks sensitive PII columns while allowing you to compute aggregate statistics and identify patterns using SQL and Snowpark functions. Columns like 'email' are masked using and columns like are masked using .
D) Utilize Snowpark to create a sampled dataset (e.g., 1% of the original data) and perform all exploratory data analysis on the sample to reduce the data volume and potential exposure of PII.
E) Apply differential privacy techniques using Snowpark to add noise to the summary statistics generated from the customer data, masking the individual contributions of each customer while revealing overall trends.

4. You've built a customer churn prediction model in Snowflake, and are using the AUC as your primary performance metric. You notice that your model consistently performs well (AUC > 0.85) on your validation set but significantly worse (AUC < 0.7) in production. What are the possible reasons for this discrepancy? (Select all that apply)

A) There's a temporal bias: the customer behavior patterns have changed since the training data was collected.
B) The AUC metric is inherently unreliable and should not be used for model evaluation.
C) The production environment has significantly more missing data compared to the training and validation environments.
D) Your training and validation sets are not representative of the real-world production data due to sampling bias.
E) Your model is overfitting to the validation data. This causes to give high performance on validation set but less accurate in the real world.

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?