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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are tasked with building a fraud detection model using Snowflake and Snowpark Python. The model needs to identify fraudulent transactions in real-time with high precision, even if it means missing some actual fraud cases. Which combination of optimization metric and model tuning strategy would be most appropriate for this scenario, considering the importance of minimizing false positives (incorrectly flagging legitimate transactions as fraudulent)?

A) Recall, optimized with a threshold adjustment to minimize false negatives.
B) Log Loss, optimized with a grid search focusing on hyperparameters that improve overall accuracy.
C) AUC-ROC, optimized with a randomized search focusing on hyperparameters related to model complexity.
D) Precision, optimized with a threshold adjustment to minimize false positives.
E) F 1-Score, optimized to balance precision and recall equally.

2. You are building a machine learning model using Snowpark Python to predict house prices. The dataset contains a feature column named 'location' which contains free-form text descriptions of house locations. You want to leverage a pre-trained Large Language Model (LLM) hosted externally to extract structured location features like city, state, and zip code from the free-form text within Snowpark. You want to minimize the data transferred out of Snowflake. Which approach is most efficient and secure?

A) Create a Snowpark User-Defined Function (UDF) that calls the external LLM API. Pass the 'location' column data to the UDF and retrieve the structured location features. Then apply the UDF directly on the Snowpark DataFrame.
B) Use the Snowflake Connector for Python to directly query the 'location' column and call the external LLM API from the connector. Then write the updated data into a new table.
C) Use Snowpark's 'createOrReplaceStage' to create an external stage pointing to the LLM API endpoint. Load the 'location' data into this stage and call the LLM API directly from the Snowflake stage using SQL.
D) Use to load the 'location' column data into a Pandas DataFrame, call the external LLM API in your Python script to enrich the location data and then use to store the enriched data back into a Snowflake table.
E) Create a Snowflake External Function that calls the external LLM API. Pass the 'location' column data to the External Function and retrieve the structured location features. Then apply the External Function directly on the Snowpark DataFrame.

3. You are working on a customer churn prediction model and are using Snowpark Feature Store. One of your features, is updated daily. You notice that your model's performance degrades over time, likely due to stale feature values being used during inference. You want to ensure that the model always uses the most up-to-date feature values. Which of the following strategies would be the MOST effective way to address this issue using Snowpark Feature Store and avoid model staleness during online inference?

A) Implement a real-time feature retrieval service that directly queries the underlying Snowflake table containing the using Snowpark, bypassing the Feature Store.
B) Configure with the attribute to manage data staleness and use the during inference, ensuring that the model always uses recent feature values.
C) Configure the Feature Group containing to automatically refresh every hour using a scheduled Snowpark Python function.
D) Define a custom User-Defined Function (UDF) in Snowflake that retrieves the 'customer_lifetime_value' from the Feature Store on demand whenever the model makes a prediction and set 'feature_retrieval_mode='fresh'S.
E) Use the method on the Feature Store client during inference, ensuring that you always pass the current timestamp.

4. A data scientist is tasked with predicting house prices using Snowflake. They have a dataset stored in a Snowflake table called 'HOUSE PRICES' with columns such as 'SQUARE FOOTAGE, 'NUM BEDROOMS, 'LOCATION_ID, and 'PRICE. They choose a Random Forest Regressor model. Which of the following steps is MOST important to prevent overfitting and ensure good generalization performance on unseen data, and how can this be effectively implemented within a Snowflake-centric workflow?

A) Tune the hyperparameters of the Random Forest model (e.g., 'max_deptm, 'n_estimators') using cross-validation. You can achieve this by splitting the 'HOUSE PRICES table into training and validation sets using Snowflake's 'QUALIFY clause or temporary tables, then train and evaluate the model within a loop or stored procedure.
B) Train the Random Forest model on the entire 'HOUSE PRICES table without splitting into training and validation sets, as this will provide the model with the most data.
C) Eliminate outliers without understanding the data properly to reduce noise.
D) Randomly select a small subset of the features (e.g., only use 'SQUARE FOOTAGE and 'NUM BEDROOMS) to simplify the model and prevent overfitting.
E) Increase the number of estimators (trees) in the Random Forest to the maximum possible value to capture all potential patterns, without cross validation.

5. You have a table in Snowflake named 'CUSTOMER DATA' with columns 'CUSTOMER D', 'PURCHASE AMOUNT', and 'RECENCY'. You want to perform feature scaling on 'PURCHASE AMOUNT' using Min-Max scaling and store the scaled values in a new column named 'SCALED PURCHASE _ AMOUNT'. Which of the following Snowflake SQL code snippets correctly implements this feature scaling? Note: Assume there are no NULL values in PURCHASE AMOUNT and you have privileges to create temporary tables and UDFs if necessary.

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

Solutions: