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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. A data science team is using Snowpark ML to train a classification model. They want to log model metadata (e.g., training parameters, evaluation metrics) and artifacts (e.g., the serialized model file) for reproducibility and model governance purposes. Which of the following approaches is the most appropriate for integrating model logging and artifact management within the Snowpark ML workflow, minimizing operational overhead?

A) Serialize the model object to a string and store it as a VARIANT column in a Snowflake table, alongside the model metadata.
B) Use a custom Python function to manually write model metadata to a Snowflake table and store the model file in a Snowflake stage.
C) Employ a separate, external model management platform (e.g., Databricks MLflow, SageMaker Model Registry) and configure Snowpark to interact with it via API calls during model training and deployment.
D) Leverage the MLflow integration within Snowpark, utilizing its ability to track experiments, log parameters and metrics, and store model artifacts directly within Snowflake stages or external storage.
E) Only track basic model performance metrics in a Snowflake table and rely on code versioning (e.g., Git) for model artifact management.

2. A data scientist is analyzing sales data in Snowflake to identify seasonal trends. The 'SALES TABLE' contains columns 'SALE DATE' (DATE) and 'SALE _ AMOUNT' (NUMBER). They want to calculate the average daily sales amount for each month and year in the dataset. Which of the following SQL queries will correctly achieve this, while also handling potential NULL values in 'SALE AMOUNT?

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

3. You're building a regression model using Snowpark Python to predict house prices. After initial training, you observe that the model consistently overestimates the prices of high-value houses and underestimates the prices of low-value houses. Given the options below, which optimization metric, along with code snippet to calculate it using Snowpark, would be most effective in addressing this specific issue?

A) Root Mean Squared Error (RMSE) - as it gives more weight to larger errors, making it suitable for addressing the underestimation/overestimation problem.

B) Mean Absolute Error MAE - as it is sensitive to outliers and will penalize large errors more heavily.

C) R-squared - as it measures the proportion of variance explained, directly addressing how well the model fits the data across all price ranges.

D) Adjusted R-squared - as it penalizes the addition of irrelevant features, improving the model's generalization ability.

E) Mean Squared Error (MSE) - as it is less sensitive to outliers than RMSE.

4. 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) 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.
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) 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.
D) 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.
E) 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 .

5. You are using Snowpark Python to process a large dataset of website user activity logs stored in a Snowflake table named 'WEB ACTIVITY'. The table contains columns such as 'USER ID', 'TIMESTAMP', 'PAGE URL', 'BROWSER', and 'IP ADDRESS'. You need to remove irrelevant data to improve model performance. Which of the following actions, either alone or in combination, would be the MOST effective for removing irrelevant data for a model predicting user conversion rates, and which Snowpark Python code snippets demonstrate these actions? Assume that conversion depends on page interaction and a model will only leverage session id and session duration.

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

Solutions: