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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q119-Q124):

NEW QUESTION # 119
You are using Snowflake ML to predict housing prices. You've created a Gradient Boosting Regressor model and want to understand how the 'location' feature (which is categorical, representing different neighborhoods) influences predictions. You generate a Partial Dependence Plot (PDP) for 'location'. The PDP shows significantly different predicted prices for each neighborhood. Which of the following actions would be MOST appropriate to further investigate and improve the model's interpretability and performance?

A. Combine the PDP for 'location' with a two-way PDP showing the interaction between 'location' and 'square_footage'.
B. Use one-hot encoding for the 'location' feature and generate individual PDPs for each one-hot encoded column.
C. Generate ICE (Individual Conditional Expectation) plots alongside the PDP to assess the heterogeneity of the relationship between 'location' and predicted price.
D. Replace the 'location' feature with a numerical feature representing the average house price in each neighborhood, calculated from historical data.
E. Remove the 'location' feature from the model, as categorical features are inherently difficult to interpret.

Answer: A,B,C

Explanation:
The correct answers are B, D, and E. B: One-hot encoding allows you to see the individual effect of each neighborhood. D: ICE plots reveal how the relationship between 'location' and predicted price varies for different individual instances, highlighting potential heterogeneity. E: A two-way PDP with 'location' and 'square_footage' helps understand if the effect of location is different for houses of different sizes. Removing 'location' (option A) might decrease performance if it's a relevant feature. Replacing it with average price (option C) introduces potential bias and data leakage if the historical data is used for both training and validation.

NEW QUESTION # 120
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 B
B. Option E
C. Option D
D. Option A
E. Option C

Answer: A,B

Explanation:
Options B and E are correct. Option B correctly uses a CTE to first categorize the sentiment and then perform one-hot encoding using the ' IFF function. This approach is efficient and readable. Option E correctly categorizes and implements one-hot encoding using Boolean expressions and casting them to integers (0 or 1). Option A attempts to perform the one-hot encoding in the same SELECT statement as the categorization, which will result in error. Because it is referencing a column it just defined, so it wont find it. Option C is incorrect because it uses both WHEN SENTIMENT SCORE < -0.5 THEN 'Negative' and 'WHEN SENTIMENT SCORE BETWEEN -0.5 AND 0.5 THEN 'Neutral" which could include duplicates. Option D is incorrect, because it includes 'ELSE 'Unknown" that is not needed, as it should only be three rules.

NEW QUESTION # 121
You have a dataset in Snowflake containing customer reviews. One of the columns, 'review_text', contains free-text customer feedback. You want to perform sentiment analysis on these reviews and include the sentiment score as a feature in your machine learning model. Furthermore, you wish to categorize the sentiment into 'Positive', 'Negative', and 'Neutral'. Given the need for scalability and efficiency within Snowflake, which methods could be employed?

A. Use a Snowflake procedure that reads all 'review_text' data, transfers data outside of Snowflake to an external server running sentiment analysis software, and then writes results back into a new table.
B. Use a Python UDF (User-Defined Function) with a pre-trained sentiment analysis library (e.g., NLTK or spaCy) to calculate the sentiment score and categorize it. Deploy the UDF in Snowflake and apply it to the 'review_text' column.
C. Create a Snowpark Python DataFrame from the Snowflake table, use a sentiment analysis library within the Snowpark environment, categorize the sentiments, and then save the resulting DataFrame back to Snowflake as a new table.
D. Create a series of Snowflake SQL queries utilizing complex string matching and keyword analysis to determine sentiment based on predefined lexicons. Categories are assigned through CASE statements.
E. Utilize Snowflake's external functions to call a pre-existing sentiment analysis API (e.g., Google Cloud Natural Language API or AWS Comprehend) passing the review text and storing the returned sentiment score and category. Ensure proper API key management and network configuration.

Answer: B,C,E

Explanation:
Options A, B, and C are viable and efficient methods for sentiment analysis within Snowflake. A Python UDF leverages the compute power of Snowflake while utilizing popular Python NLP libraries. Snowpark offers a scalable way to process data within Snowflake using Python. Snowflake's External Functions provide access to pre-built sentiment analysis APIs, which can be highly accurate but may incur costs based on API usage. Option D is not appropriate as it transfers the data out of Snowflake to perform the sentiment analysis, which is a bad design. Option E can be used as well but sentiment scores based on SQL are not going to be as accurate as calling an API or leveraging an established library.

NEW QUESTION # 122
You are analyzing a dataset of website traffic and conversions in Snowflake, aiming to understand the relationship between the number of pages visited CPAGES VISITED) and the conversion rate (CONVERSION_RATE). You perform a simple linear regression using the 'REGR SLOPE and 'REGR INTERCEPT functions. However, after plotting the data and the regression line, you observe significant heteroscedasticity (non-constant variance of errors). Which of the following actions, performed within Snowflake during the data preparation and feature engineering phase, are MOST appropriate to address this heteroscedasticity and improve the validity of your linear regression model? (Select all that apply)

A. Standardize the 'PAGES_VISITED' and 'CONVERSION_RATE variables using the and functions.Create OR REPLACE VIEW STANDARDIZED_DATA AS SELECT (PAGES_VISITED - OVER()) / OVER() AS Z PAGES_VISITED, (CONVERSION RATE -OVER()) / OVER() AS FROM ORIGINAL_DATA;
B. Apply a Box-Cox transformation to the 'CONVERSION RATE' variable. This transformation will determine the optimal lambda value using some complex SQL statistical operations. This can be approximated to log tranformation in many real life scenarios.
C. Apply a logarithmic transformation to the 'CONVERSION RATE' variable using the 'LN()' function. CREATE OR REPLACE VIEW TRANSFORMED_DATA AS SELECT PAGES VISITED, LN(CONVERSION RATE) AS LOG_CONVERSION RATE FROM ORIGINAL_DATA;
D. Calculate the weighted least squares regression by weighting each observation by the inverse of the squared predicted values from an initial OLS regression. This requires multiple SQL queries.
E. Remove outlier data points from the dataset based on the Interquartile Range (IQR) of the residuals from the original linear regression model. This requires calculating the residuals first.

Answer: B,C

Explanation:
Heteroscedasticity violates one of the assumptions of linear regression, leading to unreliable standard errors and potentially biased coefficient estimates. Option A (Logarithmic Transformation): Applying a logarithmic transformation to the dependent variable ('CONVERSION_RATE) is a common technique to stabilize the variance when the variance increases with the mean. This is particularly effective when the errors are proportional to the dependent variable. Option E (Box-Cox Transformation): A Box-Cox transformation is a more general approach to transforming the dependent variable to achieve normality and homoscedasticity. It estimates a parameter (lambda) that determines the optimal transformation. Log transformation is a special case of box cox transformation, where lambda = O. Option B describes weighted least squares regression, but directly implementing this within Snowflake SQL efficiently, including calculating the initial OLS regression and subsequent weights, would be complex and may not be practically feasible without Snowpark/Python integration. It's theoretically correct but challenging to implement in pure SQL. Option C, Standardization, addresses multicollinearity issues (if present) but doesn't directly tackle heteroscedasticity. It scales the variables but doesn't change the relationship between the mean and variance of the errors. Option D, outlier removal, can be a valid step in data preparation, but it's not a direct solution to heteroscedasticity. It might help reduce the impact of outliers on the model, but it doesn't address the underlying pattern of non-constant variance. Outlier treatment requires calculation of residuals first, which is not always easy, and may cause data loss, but it might indirectly reduce heteroscedasticity.

NEW QUESTION # 123
You are tasked with fine-tuning a Snowflake Cortex LLM model using your own labeled dataset to improve its performance on a specific sentiment analysis task related to customer reviews. You have already created a Snowflake stage 'my_stage' and uploaded your labeled data in CSV format to this stage. The labeled data contains two columns: 'review_text' and 'sentiment' (values: 'positive', 'negative', 'neutral'). Which of the following SQL commands, or sequences of commands, is MOST appropriate to initiate the fine-tuning process using the 'SNOWFLAKE.ML.FINETUNE LLM' function? Assume you have already set the necessary permissions for your role to access the model and stage.

A. Option E
B. Option D
C. Option B
D. Option A
E. Option C

Answer: A

Explanation:
The correct answer is E. 'SNOWFLAKE.ML.FINETUNE LLM' function requires 'INPUT which specifies the location of the training data, 'MODEL' which is the base LLM model from Snowflake Cortex to fine-tune and 'TASK' to specify intent of fine tuning. Option D is incorrect, it adds 'parameter' which is not required. Option B is incorrect, it is adding 'target_accuracy' which is not part of the parameters. Option A and C has custom function definitions which is incorrect.

NEW QUESTION # 124
......

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