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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. A data scientist is building a linear regression model in Snowflake to predict customer churn based on structured data stored in a table named 'CUSTOMER DATA'. The table includes features like 'CUSTOMER D', 'AGE, 'TENURE MONTHS', 'NUM PRODUCTS', and 'AVG MONTHLY SPEND'. The target variable is 'CHURNED' (1 for churned, 0 for active). After building the model, the data scientist wants to evaluate its performance using Mean Squared Error (MSE) on a held-out test set. Which of the following SQL queries, executed within Snowflake's stored procedure framework, is the MOST efficient and accurate way to calculate the MSE for the linear regression model predictions against the actual 'CHURNED values in the 'CUSTOMER DATA TEST table, assuming the linear regression model is named 'churn _ model' and the predicted values are generated by the MODEL APPLY() function?

A)

B)

C)

D)

E)

2. You are training a binary classification model in Snowflake to predict customer churn using Snowpark Python. The dataset is highly imbalanced, with only 5% of customers churning. You have tried using accuracy as the optimization metric, but the model performs poorly on the minority class. Which of the following optimization metrics would be most appropriate to prioritize for this scenario, considering the imbalanced nature of the data and the need to correctly identify churned customers, along with a justification for your choice?

A) Accuracy - as it measures the overall correctness of the model.
B) Log Loss (Binary Cross-Entropy) - as it penalizes incorrect predictions proportionally to the confidence of the prediction, suitable for probabilistic outputs.
C) Area Under the Receiver Operating Characteristic Curve (AUC-ROC) - as it measures the ability of the model to distinguish between the two classes, irrespective of the class distribution.
D) Root Mean Squared Error (RMSE) - as it is commonly used for regression problems, not classification.
E) F 1-Score - as it balances precision and recall, providing a good measure for imbalanced datasets.

3. A data science team at a retail company is using Snowflake to store customer transaction data'. They want to segment customers based on their purchasing behavior using K-means clustering. Which of the following approaches is MOST efficient for performing K-means clustering on a very large customer dataset in Snowflake, minimizing data movement and leveraging Snowflake's compute capabilities, and adhering to best practices for data security and governance?

A) Using Snowflake's Snowpark DataFrame API with a Python UDF to preprocess the data and execute the K-means algorithm within the Snowflake environment. This approach allows for scalable processing within Snowflake's compute resources with data kept securely within the governance boundaries.
B) Using a Snowflake User-Defined Function (UDF) written in Python that leverages the scikit-learn library within the UDF to perform K-means clustering directly on the data within Snowflake. Ensure the UDF is called with appropriate resource allocation (WAREHOUSE SIZE) and security context.
C) Exporting the entire customer transaction dataset from Snowflake to an external Python environment, performing K-means clustering using scikit-learn, and then importing the cluster assignments back into Snowflake as a new table. This approach involves significant data egress and potential security risks.
D) Employing only Snowflake's SQL capabilities to perform approximate nearest neighbor searches without implementing the full K-means algorithm. This compromises the accuracy and effectiveness of the clustering results.
E) Implementing K-means clustering using SQL queries with iterative JOINs and aggregations to calculate centroids and assign data points to clusters. This approach is computationally expensive and not recommended for large datasets. Moreover, security considerations are minimal.

4. You are deploying a machine learning model to Snowflake using a Python UDF. The model predicts customer churn based on a set of features. You need to handle missing values in the input data'. Which of the following methods is the MOST efficient and robust way to handle missing values within the UDF, assuming performance is critical and you don't want to modify the underlying data tables?

A) Use within the UDF to forward fill missing values. This assumes the data is ordered in a meaningful way, allowing for reasonable imputation.
B) Use within the UDF, replacing missing values with a global constant (e.g., 0) defined outside the UDF. This constant is pre-calculated based on the training dataset's missing value distribution.
C) Raise an exception within the UDF when a missing value is encountered, forcing the calling application to handle the missing values.
D) Pre-process the data in Snowflake using SQL queries to replace missing values with the mean for numerical features and the mode for categorical features before calling the UDF.
E) Implement a custom imputation strategy using 'numpy.where' within the UDF, basing the imputation value on a weighted average of other features in the row.

5. You are training a binary classification model in Snowflake using Snowpark to predict customer churn. The dataset contains a mix of numerical and categorical features, and you've identified that the 'COUNTRY' feature has high cardinality. You observe that your model performs poorly for less frequent countries. To address this, you decide to up-sample the minority classes within the 'COUNTRY' feature before training. Which combination of techniques would be MOST appropriate and computationally efficient for up-sampling in this scenario within Snowflake, considering you are working with a large dataset and want to minimize data shuffling across the network?

A) Use Snowpark's 'DataFrame.groupBy()" and 'DataFrame.count()' to identify minority countries. Then, for each minority country, use DataFrame.unionByName()' to combine the original data with multiple copies of the minority country's data, created using 'DataFrame.sample()' with replacement. This minimizes data movement within Snowflake.
B) Use the 'SAMPLE clause in Snowflake SQL with 'REPLACE' for each minority country, creating separate temporary tables and then combining them with UNION ALL'. This is efficient for small datasets but scales poorly with high cardinality.
C) Use a stored procedure written in Python to iterate through each unique country, identify minority countries, and then use Snowpark to up-sample those countries using 'DataFrame.sample()' with replacement. This offers the most flexibility but introduces significant overhead due to context switching.
D) Utilize Snowflake UDFs (User-Defined Functions) written in Java to perform stratified sampling on the 'COUNTRY' feature, ensuring each minority class is adequately represented in the up-sampled dataset. UDFs allow for complex logic but can be challenging to debug within Snowflake.
E) Leverage Snowpark's 'DataFrame.collect()' to bring the entire dataset to the client machine, then use Python's scikit-learn library for up-sampling. This is suitable only for small datasets as it incurs significant network overhead.

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