Snowflake DSA-C03 : SnowPro Advanced: Data Scientist Certification Exam

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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You have a table 'PRODUCT SALES in Snowflake with columns: 'PRODUCT (INT), 'SALE_DATE (DATE), 'SALES_AMOUNT (FLOAT), and 'PROMOTION FLAG' (BOOLEAN). You need to perform the following data preparation steps using Snowpark SQLAPI:

A) Creating a feature that returns 1 if there is a PROMOTION_FLAG of True and SALES_AMOUNT > 1000, and zero otherwise
B) All of the above.
C) Converting 'SALE_DATE to a quarterly representation (e.g., '2023-QI').
D) Handling missing 'SALES_AMOUNT values by imputing them with the average 'SALES_AMOUNT' for the same 'PRODUCT_ID during the previous month. If there's no data for the previous month, use the overall average for that
E) Creating a new feature representing the percentage change in 'SALES_AMOUNT compared to the previous day for the same 'PRODUCT_ID. Handle the first day of each 'PRODUCT by setting 'SALES_GROWTH' to O.

2. Your team has deployed a machine learning model to Snowflake for predicting customer churn. You need to implement a robust metadata tagging strategy to track model lineage, performance metrics, and usage. Which of the following approaches are the MOST effective for achieving this within Snowflake, ensuring seamless integration with model deployment pipelines and facilitating automated retraining triggers based on data drift?

A) Relying solely on manual documentation and spreadsheets to track model metadata, as automated solutions introduce unnecessary complexity and potential errors.
B) Utilizing Snowflake's INFORMATION SCHEMA views to extract metadata about tables, views, and stored procedures, and then writing custom SQL scripts to generate reports and track model lineage. Combine this with Snowflake's data masking policies to control access to sensitive metadata.
C) Using Snowflake's built-in tag functionality to tag tables, views, and stored procedures related to the model. Implementing custom Python scripts using Snowflake's Python API (Snowpark) to automatically apply tags during model deployment and retraining based on predefined rules and data quality checks.
D) Leveraging a third-party metadata management tool that integrates with Snowflake and provides a centralized repository for model metadata, lineage tracking, and data governance. This tool should support automated tag propagation and data drift monitoring. Use Snowflake external functions to trigger alerts based on metadata changes.
E) Storing model metadata in a separate relational database (e.g., PostgreSQL) and using Snowflake external tables to access the metadata information. Implement custom stored procedures to synchronize metadata between Snowflake and the external database.

3. You are developing a model to predict equipment failure in a factory using sensor data stored in Snowflake. The data is partitioned by 'EQUIPMENT ID' and 'TIMESTAMP. After initial model training and cross-validation using the following code snippet:

You observe significant performance variations across different equipment groups when evaluating on out-of-sample data'. Which of the following strategies could you employ to address this issue within the Snowflake environment to improve the model's generalization ability across all equipment?

A) Implement a hyperparameter search using 'SYSTEM$OPTIMIZE_MODEL' with a wider range of parameters for each 'EQUIPMENT_ID individually, creating a separate model for each 'EQUIPMENT ID.
B) Create seperate models per equipment ID. For each equipment ID, split data into training and testing data. For each equipment ID, use 'SYSTEM$OPTIMIZE MODEL' to perform hyper parameter search individually. Train and Deploy the model at equipement ID Level.
C) Retrain the model with additional feature engineering to create interaction terms between 'EQUIPMENT_ID' and other relevant sensor features to capture equipment-specific patterns. For instance, you can one hot encode and add to model and include in 'INPUT DATA'.
D) Implement cross-validation at the partition level by splitting 'TRAINING_DATX into train and test sets before creating the model, and then using the 'FIT' command to train on the train set and 'PREDICT to evaluate on the test set, repeating for each partition.
E) Increase the overall size of the "TRAINING_DATR to include more historical data for all equipment, assuming this will balance the representation of each EQUIPMENT ID'

4. You are a data scientist working with a large dataset of customer transactions stored in Snowflake. You need to identify potential fraud using statistical summaries. Which of the following approaches would be MOST effective in identifying unusual spending patterns, considering the need for scalability and performance within Snowflake?

A) Calculate the average transaction amount and standard deviation for each customer using window functions in SQL. Flag transactions that fall outside of 3 standard deviations from the customer's mean.
B) Sample a subset of the data, calculate descriptive statistics using Snowpark Python and the 'describe()' function, and extrapolate these statistics to the entire dataset.
C) Use Snowflake's native anomaly detection functions (if available, and configured for streaming) to detect anomalies based on transaction amount and frequency, grouped by customer ID.
D) Implement a custom UDF (User-Defined Function) in Java to calculate the interquartile range (IQR) for each customer's transaction amounts and flag transactions as outliers if they are below QI - 1.5 IQR or above Q3 + 1.5 IQR.
E) Export the entire dataset to a Python environment, use Pandas to calculate the average transaction amount and standard deviation for each customer, and then identify outliers based on a fixed threshold.

5. You are building a customer churn prediction model in Snowflake using Snowflake ML. After training, you need to evaluate the model's performance and identify areas for improvement. Given the following table 'PREDICTIONS' contains predicted probabilities and actual churn labels, which SQL query effectively calculates both precision and recall for the churn class (where 'CHURN = 1')?

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

Solutions: