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Snowflake DSA-C03 Exam Overview:

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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are tasked with building a Python stored procedure in Snowflake to train a Gradient Boosting Machine (GBM) model using XGBoost.
The procedure takes a sample of data from a large table, trains the model, and stores the model in a Snowflake stage. During testing, you notice that the procedure sometimes exceeds the memory limits imposed by Snowflake, causing it to fail. Which of the following techniques can you implement within the Python stored procedure to minimize memory consumption during model training?

A) Reduce the sample size of the training data and increase the number of boosting rounds to compensate for the smaller sample. Use the 'predict_proba' method to avoid storing probabilities for all classes.
B) Write the training data to a temporary table in Snowflake, then use Snowflake's external functions to train the XGBoost model on a separate compute cluster outside of Snowflake. Then upload the model to snowflake stage.
C) Implement XGBoost's 'early stopping' functionality with a validation set to prevent overfitting. If the stored procedure exceeds the memory limits, the model cannot be saved. Always use larger virtual warehouse.
D) Use the 'hist' tree method in XGBoost, enable gradient-based sampling ('gosS), and carefully tune the 'max_depth' and parameters to reduce memory usage during tree construction. Convert all features to numerical if possible.
E) Convert the Pandas DataFrame used for training to a Dask DataFrame and utilize Dask's distributed processing capabilities to train the XGBoost model in parallel across multiple Snowflake virtual warehouses.

2. You are working with a Snowflake table named 'CUSTOMER DATA' that contains personally identifiable information (PII), including customer names, email addresses, and phone numbers. Your team needs to perform exploratory data analysis on this data to understand customer demographics and behavior. However, you must ensure that the PII is protected and that only authorized personnel can access the sensitive information. Which of the following strategies should you implement in Snowflake to achieve secure EDA?

A) Grant 'SELECT privileges on the 'CUSTOMER DATA' table to all data scientists, and rely on them to avoid querying PII columns directly.
B) Apply dynamic data masking to the entire 'CUSTOMER_DATA' table, masking all columns by default, and provide decryption keys only to authorized users.
C) Create a copy of the 'CUSTOMER DATA table without the PII columns and grant 'SELECT' privileges on this copy to the data scientists. Use masking policies on the original table.
D) Create a view on top of that excludes the PII columns (e.g., name, email, phone). Grant 'SELECT privileges on this view to data scientists. Also implement data masking policies on the 'CUSTOMER DATA' table for the PII columns and grant 'SELECT on the table to specific roles requiring access to the masked values.
E) Use transient tables to store the customer data after PII is obfuscated, drop the table and reload new data daily.

3. You are tasked with training a machine learning model within Snowflake using a Python UDTF. The UDTF is intended to process incoming sales data, calculate features, and update the model incrementally. The model is a simple linear regression using scikit-learn. Your initial attempt fails with a 'ModuleNotFoundError: No module named 'sklearn" error within the UDTF. You have already confirmed that scikit-learn is available in your Anaconda channel and specified it during session creation. Which of the following actions would MOST directly address this issue and allow the UDTF to successfully import and use scikit-learn?

A) Ensure that the Anaconda channel containing 'sklearn' is explicitly activated at the account level using the 'ALTER ACCOUNT command. Verify the channel is listed in 'SHOW CHANNELS'.
B) When creating the UDTF, use the 'PACKAGES' parameter to explicitly specify the 'skiearn' package. For example: 'CREATE OR REPLACE FUNCTION RETURNS TABLE LANGUAGE PYTHON RUNTIME_VERSION = '3.8' PACKAGES = ('snowflake-snowpark-python','scikit-learn') ...
C) Include ' import snowflake.snowpark; session = snowflake.snowpark.session.get_active_session()' within the UDTF code to explicitly initialize the Snowpark session before importing sklearn. Ensure that scikit-learn is included in the 'imports' argument of the 'create_dataframe' method.
D) Explicitly copy the 'sklearn' directory and its dependencies directly into the same directory as your UDTF definition script on the Snowflake stage, then reference them using relative paths within the UDTF.
E) Recreate the Anaconda environment and ensure that the 'sklearn' package is installed specifically within the environment's 'site-packages' directory. Then, recreate the Snowflake session.

4. A data scientist is tasked with predicting customer churn for a telecommunications company using Snowflake. The dataset contains call detail records (CDRs), customer demographic information, and service usage data'. Initial analysis reveals a high degree of multicollinearity between several features, specifically 'total_day_minutes', 'total_eve_minutes', and 'total_night_minutes'. Additionally, the 'state' feature has a large number of distinct values. Which of the following feature engineering techniques would be MOST effective in addressing these issues to improve model performance, considering efficient execution within Snowflake?

A) Calculate the Variance Inflation Factor (VIF) for each CDR feature and drop the feature with the highest VIE Apply frequency encoding to the 'state' feature.
B) Use a variance threshold to remove highly correlated CDR features and create a feature representing the geographical region (e.g., 'Northeast', 'Southwest') based on the 'state' feature using a custom UDF.
C) Apply min-max scaling to the CDR features to normalize them and use label encoding for the 'state' feature. Train a decision tree model, as it is robust to multicollinearity.
D) Apply Principal Component Analysis (PCA) to reduce the dimensionality of the CDR features ('total_day_minutes', 'total_eve_minutes', 'total_night_minutes') and use one-hot encoding for the 'state' feature.
E) Create interaction features by multiplying 'total_day_minutes' with 'customer_service_calls' and applying a target encoding to the 'state' feature.

5. 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) 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.
B) 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.
C) First extracting all the data from snowflake into pandas dataframe and then use pivoting and other pandas operations to convert to the needed format.
D) 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.
E) 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.

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