Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. A financial institution aims to detect fraudulent transactions using a Supervised Learning model deployed in Snowflake. They have a dataset with transaction details, including amount, timestamp, merchant category, and customer ID. The target variable is 'is_fraudulent' (0 or 1). They are considering different Supervised Learning algorithms. Which of the following algorithms would be MOST suitable for this fraud detection task, considering the need for interpretability, scalability, and the potential for imbalanced classes, and what specific strategies can be employed within Snowflake to handle the class imbalance?

A) K-Nearest Neighbors (KNN), because it is simple to implement and doesn't require extensive training.
B) Decision Tree or Random Forest, combined with techniques like oversampling the minority class (fraudulent transactions) within Snowflake using SQL or UDFs to balance the dataset before training. These models provide reasonable interpretability and can handle non-linear relationships effectively.
C) Linear Regression, because it's computationally efficient and easy to understand, even though fraud detection is a classification problem.
D) Support Vector Machine (SVM) with a radial basis function (RBF) kernel, as it can capture complex non-linear relationships without concern for interpretability.
E) Naive Bayes, because it requires no hyperparameter tuning and works well on numerical data.

2. You are developing a data transformation pipeline in Python that reads data from Snowflake, performs complex operations using Pandas DataFrames, and writes the transformed data back to Snowflake. You've implemented a function, 'transform data(df)', which processes a Pandas DataFrame. You want to leverage Snowflake's compute resources for the DataFrame operations as much as possible, even for intermediate transformations before loading the final result. Which of the following strategies could you employ to optimize this process, assuming you have a configured Snowflake connection "conn"?

A) Use 'snowflake.connector.pandas_tools.write_pandas(conn, df, table_name, auto_create_table=Truey to write the transformed DataFrame to Snowflake and let Snowflake handle the transformations using SQL.
B) Create a series of Snowflake UDFs that perform the individual transformations within Snowflake, load the data into Pandas DataFrames, apply UDFs on these DataFrames, and use to upload to Snowflake.
C) Read the entire Snowflake table into a single Pandas DataFrame, apply , and then write the entire transformed DataFrame back to Snowflake.
D) Chunk the Snowflake table into smaller DataFrames using 'fetchmany()' , apply to each chunk, and then append each transformed chunk to a Snowflake table using multiple INSERT statements. Call columns=[col[0] for col in cur.description]))'
E) Use Snowpark Python DataFrame API to perform the transformation directly on Snowflake's compute and then load results into the same table. Call 'df_snowpark = session.create_dataframe(df)'.

3. You have a Snowflake Model Registry set up and are managing multiple versions of a machine learning model. You want to programmatically retrieve a specific version of the model and load it for inference within a Snowflake Snowpark Python UDE Assume your registry name is 'my_registry', the model name is 'credit risk_model', and you want to retrieve version 'v2'. How would you achieve this using Snowpark Python?

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

4. A data scientist is building a model in Snowflake to predict customer churn. They have a dataset with features like 'age', 'monthly_spend', 'contract_length', and 'complaints'. The target variable is 'churned' (0 or 1). They decide to use a Logistic Regression model. However, initial performance is poor. Which of the following actions could MOST effectively improve the model's performance, considering best practices for Supervised Learning in a Snowflake environment focused on scalable and robust deployment?

A) Fit a deep neural network with numerous layers directly within Snowflake without any data preparation, as this will automatically extract complex patterns.
B) Reduce the number of features by randomly removing some columns, as this always prevents overfitting.
C) Implement feature scaling (e.g., StandardScaler or MinMaxScaler) on numerical features within Snowflake, before training the model. Leverage Snowflake's user-defined functions (UDFs) for transformation and then train the model.
D) Increase the learning rate significantly to speed up convergence during training.
E) Ignore missing values in the dataset as the Logistic Regression model will handle it automatically without skewing the results.

5. You're working with a Snowflake stage named that contains several versions of your machine learning model, named 'model_vl .pkl' , 'model_v2.pkl' , and You want to programmatically list all files in the stage and retrieve the creation time of the latest version (i.e., using SnowSQL. Which of the following approaches is most efficient and correct?

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

Solutions: