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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are working on a customer churn prediction model and are using Snowpark Feature Store. One of your features, is updated daily. You notice that your model's performance degrades over time, likely due to stale feature values being used during inference. You want to ensure that the model always uses the most up-to-date feature values. Which of the following strategies would be the MOST effective way to address this issue using Snowpark Feature Store and avoid model staleness during online inference?

A) Define a custom User-Defined Function (UDF) in Snowflake that retrieves the 'customer_lifetime_value' from the Feature Store on demand whenever the model makes a prediction and set 'feature_retrieval_mode='fresh'S.
B) Configure the Feature Group containing to automatically refresh every hour using a scheduled Snowpark Python function.
C) Use the method on the Feature Store client during inference, ensuring that you always pass the current timestamp.
D) Implement a real-time feature retrieval service that directly queries the underlying Snowflake table containing the using Snowpark, bypassing the Feature Store.
E) Configure with the attribute to manage data staleness and use the during inference, ensuring that the model always uses recent feature values.

2. You are building a multi-class classification model in Snowflake to predict the category of customer support tickets (e.g., 'Billing', 'Technical Support', 'Sales Inquiry', 'Account Management', 'Feature Request') based on the ticket's text content. The initial model evaluation shows an overall accuracy of 75%, but the 'Feature Request' category has a significantly lower precision and recall compared to other categories. Which of the following strategies would be MOST effective in addressing this issue, considering the limitations and advantages of Snowflake's data processing capabilities and typical machine learning practices?

A) Increase the threshold for classifying a ticket as 'Feature Request' to improve precision, even if it further reduces recall. This prioritizes accurate identification of feature requests over capturing all of them.
B) All of the above.
C) Apply a cost-sensitive learning approach during model training, assigning a higher misclassification cost to errors involving the 'Feature Request' category. This encourages the model to prioritize correctly classifying feature requests.
D) Engineer new features specifically designed to improve the model's ability to distinguish 'Feature Request' tickets from other categories. This could involve creating sentiment scores for 'innovation' or using topic modeling to identify key themes related to feature requests.
E) Oversample the 'Feature Request' category in the training dataset before training the model. This involves creating synthetic data points or duplicating existing data to balance the class distribution. This can be done using SQL and Snowflake's internal stage for storing temporary data before training.

3. You have a table in Snowflake named 'CUSTOMER DATA' with columns 'CUSTOMER D', 'PURCHASE AMOUNT', and 'RECENCY'. You want to perform feature scaling on 'PURCHASE AMOUNT' using Min-Max scaling and store the scaled values in a new column named 'SCALED PURCHASE _ AMOUNT'. Which of the following Snowflake SQL code snippets correctly implements this feature scaling? Note: Assume there are no NULL values in PURCHASE AMOUNT and you have privileges to create temporary tables and UDFs if necessary.

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

4. A retail company, 'GlobalMart,' wants to optimize its product placement strategy in its physical stores. They have transactional data stored in Snowflake, capturing which items are purchased together in the same transaction. They aim to use association rule mining to identify frequently co-occurring items. Given the following simplified transactional data in a Snowflake table named 'SALES TRANSACTIONS:

Which of the following SQL-based approaches, combined with Snowpark Python for association rule generation (using a library like 'mlxtend'), would be the MOST efficient and scalable way to prepare this data for association rule mining, specifically focusing on converting it into a transaction-item matrix suitable for algorithms like Apriori? Assume 'spark' is a 'snowpark.Session' object connected to your Snowflake environment.

A) Employing a custom UDF (User-Defined Function) written in Java or Scala that directly processes the transactional data within Snowflake and outputs the transaction-item matrix in a format suitable for Snowpark. This offloads processing to compiled code within Snowflake, maximizing performance.
B) Using Snowpark's 'DataFrame.groupBy(V and functions to aggregate items by transaction ID, then pivoting the data using to create the transaction-item matrix. This approach requires loading all data into the Snowpark DataFrame before pivoting.
C) Utilizing Snowflake's SQL function within a stored procedure to concatenate items purchased in each transaction into a string, then processing the string using Python in Snowpark to create the transaction-item matrix. This approach minimizes data transfer but introduces string parsing overhead in Python.
D) Creating a temporary table in Snowflake using a SQL query that aggregates items by transaction and represents them in a format suitable for Snowpark's 'mlxtend' library. Then load this temporary table into a Snowpark DataFrame and use it as input to the Apriori algorithm.
E) First extracting all the data from snowflake into pandas dataframe and then use pivoting and other pandas operations to convert to the needed format.

5. You are working on a fraud detection model and need to prepare transaction data'. You have two tables: 'transactions' (transaction_id, customer_id, transaction_date, amount, merchant_id) and (merchant_id, city, state). You need to perform the following data cleaning and feature engineering steps using Snowpark: 1. Remove duplicate transactions based on 'transaction_id'. 2.
Join the 'transactions' table with the 'merchant_locations table to add city and state information to each transaction. 3. Create a new feature called 'amount_category' based on the transaction amount, categorized as 'Low', 'Medium', or 'High'. 4. The categorization thresholds are defined as follows: 'LoW: amount < 50 'Medium': 50 amount < 200 'High': amount >= 200 Which of the following statements about performing these operations using Snowpark are accurate?

A) A LEFT JOIN should be used to join the 'transactions' and 'merchant_location' tables to ensure that all transactions are included, even if some merchant IDs are not present in the 'merchant_location' table.
B) The construct in Snowpark can be used to create the 'amount_category' feature directly within the DataFrame transformation without needing a UDF
C) Removing duplicate transactions can be efficiently done using the method on the Snowpark DataFrame, specifying 'transaction_id' as the subset. Creating the amount categories can be completed using the 'when' clause with multiple 'otherwise' clauses.
D) You can register SQL UDF to calculate the 'amount_category' using 'CASE WHEN' statement
E) Removing duplicate transactions can be efficiently done using the method on the Snowpark DataFrame, specifying 'transaction_id' as the subset. Creating the amount categories requires use of a User-Defined Function (UDF) as the logic can't be efficiently embedded in a single 'when' clause.

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