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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are working with a large sales transaction dataset in Snowflake, stored in a table named 'SALES DATA'. This table contains columns such as 'TRANSACTION_ID (unique identifier), 'CUSTOMER_ID', 'PRODUCT_ID, 'TRANSACTION_DATE' , and 'AMOUNT'. Due to a system error, some transactions were duplicated in the table. Your goal is to remove these duplicates efficiently using Snowpark for Python. You want to use the 'window.partitionBy()' and functions. Which of the following code snippets correctly removes duplicates based on all columns, while also creating a new column 'ROW NUM' to indicate the row number within each partition?

A)

B)

C)

D)

E)

2. You are developing a Snowflake Native App that leverages Snowflake Cortex for text summarization. The app needs to process user-provided text input in real-time and return a summarized version. You want to expose this functionality as a secure and scalable REST API endpoint within the Snowflake environment. Which of the following strategies are MOST suitable for achieving this, considering best practices for security and performance?

A) Develop a Snowflake Native App containing a Python UDF that calls 'SNOWFLAKCORTEX.SUMMARIZE function, and expose it as a REST API endpoint using Snowflake's API Integration feature within the app package.
B) Develop a Snowflake Native App that includes a Java UDF that calls 'SNOWFLAKE.CORTEX.SUMMARIZE and expose a REST API using Snowflake's built-in REST API capabilities within the Native App framework.
C) Write a Snowflake Stored Procedure using Javascript to invoke the 'SNOWFLAKE.CORTEX.SUMMARIZE function, deploy the procedure to a Snowflake stage, and then trigger it via an AWS Lambda function integrated with Snowflake.
D) Utilize a Snowflake Stored Procedure written in SQL that invokes the 'SNOWFLAKE.CORTEX.SUMMARIZE' function, and then create a Snowflake API Integration to expose the stored procedure as a REST endpoint.
E) Create a Snowflake External Function using Python that directly calls the 'SNOWFLAKE.CORTEX.SUMMARIZE' function and expose this function via a REST API gateway outside of Snowflake.

3. You are tasked with identifying fraudulent transactions from unstructured log data stored in Snowflake. The logs contain various fields, including timestamps, user IDs, and transaction details embedded within free-text descriptions. You plan to use a supervised learning approach, having labeled a subset of transactions as 'fraudulent' or 'not fraudulent.' Which of the following methods best describes the extraction and processing of this data for training a machine learning model within Snowflake?

A) Treat the unstructured log description as a categorical feature and directly apply one-hot encoding within Snowflake, then train a classification model. Due to high dimensionality perform PCA for dimensionality reduction before training.
B) Export the entire log data to an external machine learning platform (e.g., AWS SageMaker) and perform feature extraction, NLP processing, and model training there. Import the trained model back into Snowflake as a UDF for prediction.
C) Use regular expressions within a Snowflake UDF to extract relevant information (e.g., amount, item description) from the log descriptions. Convert extracted data into numerical features using one-hot encoding within the UDF. Then, train a model using the extracted numerical features directly within Snowflake using SQL extensions for machine learning.
D) Use a combination of regular expressions and natural language processing (NLP) techniques within Snowflake UDFs to extract key features such as transaction amounts, product categories, and sentiment scores from the log descriptions. Then, combine these extracted features with other structured data (e.g., user demographics) and train a classification model using these features. The NLP steps include tokenization, stop word removal, and TF-IDF vectorization.
E) Extract the entire log description field and train a word embedding model (e.g., Word2Vec) on the entire dataset. Average the word vectors for each transaction's log description to create a document vector. Train a classification model (e.g., Random Forest) on these document vectors within Snowflake.

4. 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 E
B) Option B
C) Option D
D) Option A
E) Option C

5. You have a structured dataset in Snowflake containing customer information and purchase history. You aim to build a multi-class classification model to predict customer churn, categorizing customers into 'Low Risk', 'Medium Risk', and 'High Risk' of churning. After training the model, you want to evaluate its performance. Which of the following metrics and evaluation techniques, when used together, provide the MOST comprehensive understanding of the model's performance across all churn risk categories, especially when dealing with potential class imbalance?

A) Log Loss (Cross-Entropy Loss), Gini Coefficient, and Kolmogorov-Smirnov (KS) statistic.
B) Overall Accuracy, Precision, Recall, F I-Score for each class, and Confusion Matrix.
C) Area Under the ROC Curve (AUC-ROC) for each class (one-vs-rest approach), Precision-Recall Curve for each class, and Cumulative Accuracy Profile (CAP) curve.
D) Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (Coefficient of Determination).
E) Only Overall Accuracy and a confusion Matrix.

Solutions: