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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. 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) 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.
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) 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.
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 .

2. You're deploying a pre-built image classification model hosted on a REST API endpoint, and you need to integrate it with Snowflake to classify images stored in cloud storage accessible via an external stage named 'IMAGE STAGE. The API expects image data as a base64 encoded string in the request body. Which SQL query snippet demonstrates the correct approach for calling the external function 'CLASSIFY IMAGE and incorporating the base64 encoding?

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

3. A data scientist is analyzing website traffic data stored in Snowflake. The data includes daily page views for different pages. The data scientist suspects that the variance of page views for a particular page, 'home', has significantly increased recently. Which of the following steps and Snowflake SQL queries could be used to identify a potential change in the variance of 'home' page views over time (e.g., comparing variance before and after a specific date)? Select all that apply.

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

4. You are building a fraud detection model for an e-commerce platform. One of the features is 'purchase_amount', which ranges from $1 to $10,000. The data has a skewed distribution with many small purchases and a few very large ones. You need to normalize this feature for your model, which uses gradient descent. Which normalization technique(s) would be most suitable in Snowflake, considering the data characteristics and the need to handle potential future outliers?

A) Unit Vector normalization (L2 Normalization) using SQL:

B) Robust scaling using interquartile range (IQR) in a stored procedure with Python:

C) Z-score standardization using the following SQL:

D) Min-Max scaling using the following SQL:

E) Power Transformer (e.g., Yeo-Johnson) implemented with Snowpark Python:

5. You've developed a binary classification model using Snowpark ML to predict customer subscription renewal (0 for churn, 1 for renew). You want to visualize feature importance using a permutation importance technique calculated within Snowflake. You perform feature permutation and calculate the decrease in model performance (e.g., AUC) after each permutation. Suppose the following query represents the results of this process:

The 'feature_importance_results' table contains the following data:

Based on this output, which of the following statements are the MOST accurate interpretations regarding feature impact and model behavior?

A) Permutation importance only reveals the importance of features within the current model. Different models trained with different features or algorithms might have different feature rankings.
B) The 'contract_length' and 'monthly_charges' features are equally important.
C) The 'support_calls' feature is the least important feature; removing it entirely from the model will have little impact on its AUC performance.
D) Increasing the 'contract_length' for customers will always lead to a higher probability of renewal. However, there could be correlation between contract length and monthly charges.
E) The 'contract_length' feature is the most important feature for the model's predictive performance; shuffling it causes the largest drop in AUC.

Solutions: