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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are working with a dataset in Snowflake containing customer reviews stored in a 'REVIEWS' table. The 'SENTIMENT SCORE column contains continuous values ranging from -1 (negative) to 1 (positive). You need to create a new column, 'SENTIMENT CATEGORY, based on the following rules: 'Negative': 'SENTIMENT SCORE < -0.5 'Neutral': -0.5 'SENTIMENT SCORE 0.5 'Positive': 'SENTIMENT SCORE > 0.5 You also want to binarize this 'SENTIMENT CATEGORY column into three separate columns: 'IS NEGATIVE, 'IS NEUTRAL', and 'IS POSITIVE. Which of the following SQL statements correctly implements both the categorization and subsequent binarization?

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

2. You have deployed a custom model using Snowpark within Snowflake. The model is designed to predict customer churn, and you've wrapped it in a User-Defined Function (UDF) for easy use. The UDF takes several customer features as input and returns a churn probability. However, you notice the UDF's performance is slow, especially when scoring large batches of customers. Which of the following strategies would be most effective in optimizing the performance of your model deployment within Snowflake? Assume the UDF is already using vectorization techniques.

A) Implement row-level security on the input data. This enhances security and implicitly improves query performance because the model only processes authorized data.
B) Increase the warehouse size used by Snowflake. This provides more resources for the UDF execution.
C) Cache the results of the UDF using Snowflake's result caching feature. This will avoid re-executing the UDF for the same input values.
D) Re-write the UDF in SQL instead of Snowpark to avoid the overhead of the Snowpark API.
E) Utilize a vectorized UDF that can process multiple rows in a single call, further leveraging Snowflake's parallel processing capabilities. Ensure it supports the correct data types for both input and output. Consider using a Pandas UDF if Python is the underlying language.

3. You have deployed a sentiment analysis model on AWS SageMaker and want to integrate it with Snowflake using an external function. You've created an API integration object. Which of the following SQL statements is the most secure and efficient way to create an external function that utilizes this API integration, assuming the model expects a JSON payload with a 'text' field, the API integration is named 'sagemaker_integration' , the SageMaker endpoint URL is 'https://your-sagemaker-endpoint.com/invoke' , and you want the Snowflake function to be named 'predict_sentiment'?

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

4. You are developing a regression model in Snowflake using Snowpark to predict house prices based on features like square footage, number of bedrooms, and location. After training the model, you need to evaluate its performance. Which of the following Snowflake SQL queries, used in conjunction with the model's predictions stored in a table named 'PREDICTED PRICES, would be the most efficient way to calculate the Root Mean Squared Error (RMSE) using Snowflake's built-in functions, given that the actual prices are stored in the 'ACTUAL PRICES' table?

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

5. Which of the following statements about Z-tests and T-tests are generally true? Select all that apply.

A) A T-test has fewer degrees of freedom compared to the Z-test, making it more robust to outliers.
B) Both Z-tests and T-tests assume that the data is non-normally distributed.
C) A T-test is generally used when the sample size is large (n > 30) and the population standard deviation is known.
D) As the sample size increases, the T-distribution approaches the standard normal (Z) distribution.
E) A Z-test requires knowing the population standard deviation, while a T-test estimates it from the sample data.

Solutions: