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

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You have developed a customer churn prediction model using Python and deployed it as a Snowflake UDE You are monitoring its performance and notice a significant drop in accuracy over time. To address this, you need to implement automated model retraining with regular validation. Which of the following steps and validation techniques are MOST critical for ensuring the retrained model is effective and avoids overfitting to recent data? (Select THREE)

A) Implement a data drift detection mechanism. Monitor the distribution of input features over time and trigger retraining if significant drift is detected using tools such as Snowflake's Anomaly Detection features or custom drift metrics calculated in SQL.
B) Monitor the model's performance on a live dataset and trigger retraining only when the performance drops below a predefined threshold, using metrics like accuracy, precision, or recall. Save Model Performance to 'MODEL_PERFORMANCE.
C) Update the UDF in place using 'CREATE OR REPLACE FUNCTION' immediately after retraining completes, regardless of the validation results.
D) Use cross-validation techniques (e.g., k-fold cross-validation) during the retraining process to estimate the model's performance on unseen data and prevent overfitting. Evaluate on a held-out validation set.
E) Retrain the model using the entire available dataset, as this will maximize the amount of data the model learns from.

2. A marketing analyst is building a propensity model to predict customer response to a new product launch. The dataset contains a 'City' column with a large number of unique city names. Applying one-hot encoding to this feature would result in a very high-dimensional dataset, potentially leading to the curse of dimensionality. To mitigate this, the analyst decides to combine Label Encoding followed by binarization techniques. Which of the following statements are TRUE regarding the benefits and challenges of this combined approach in Snowflake compared to simply label encoding?

A) While label encoding itself adds an ordinal relationship, applying binarization techniques like binary encoding (converting the label to binary representation and splitting into multiple columns) after label encoding will remove the arbitrary ordinal relationship.
B) Binarizing a label encoded column using a simple threshold (e.g., creating a 'high_city_id' flag) addresses the curse of dimensionality by reducing the number of features to one, but it loses significant information about the individual cities.
C) Label encoding introduces an arbitrary ordinal relationship between the cities, which may not be appropriate. Binarization alone cannot remove this artifact.
D) Binarization following label encoding may enhance model performance if a specific split based on a defined threshold is meaningful for the target variable (e.g., distinguishing between cities above/below a certain average income level related to marketing success).
E) Label encoding followed by binarization will reduce the memory required to store the 'City' feature compared to one-hot encoding, and Snowflake's columnar storage optimizes storage for integer data types used in label encoding.

3. You have a Snowflake table called 'website visits' with columns 'user id', 'visit_date', and You need to identify users who consistently spend a large amount of time on specific page URLs. You want to calculate the average time spent per user on each page URL and then find the top 10 page URLs where users, on average, spend the most time. Which of the following approaches is the MOST efficient and accurate for achieving this in Snowflake?

A)

B)

C)

D)

E)

4. You are developing a regression model in Snowflake to predict housing prices. You've trained a model using Snowflake ML functions and now need to rigorously validate its performance. You have a separate validation dataset stored in a table named 'HOUSING VALIDATION'. Which of the following SQL statements, when executed in Snowflake, would accurately calculate the Root Mean Squared Error (RMSE) of your model's predictions against the actual prices in the validation dataset, assuming your model is named 'HOUSING PRICE MODEL' and the prediction function generated by CREATE SNOWFLAKE.ML.FORECAST is called PREDICT?

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

5. You are working on a customer churn prediction project. One of the features you want to normalize is 'customer_age'. However, a Snowflake table constraint ensures that all 'customer_age' values are between 0 and 120 (inclusive). Furthermore, you want to avoid using any stored procedures and prefer a pure SQL approach for data transformation. Considering these constraints, which normalization technique and associated SQL query is the most appropriate in Snowflake for this scenario, guaranteeing that the scaled values remain within a predictable range?

A) Box-Cox transformation:

B) Z-score standardization:

C) Min-Max scaling directly to the range [0, 1] using the known bounds (0 and 120):

D) Min-Max scaling to the range [0, 1]:

E) Z-score standardization after clipping values outside 1 and 99 percentile: