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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q106-Q111):

NEW QUESTION # 106
You've built a customer churn prediction model in Snowflake, and are using the AUC as your primary performance metric. You notice that your model consistently performs well (AUC > 0.85) on your validation set but significantly worse (AUC < 0.7) in production. What are the possible reasons for this discrepancy? (Select all that apply)

A. Your model is overfitting to the validation data. This causes to give high performance on validation set but less accurate in the real world.
B. Your training and validation sets are not representative of the real-world production data due to sampling bias.
C. The production environment has significantly more missing data compared to the training and validation environments.
D. There's a temporal bias: the customer behavior patterns have changed since the training data was collected.
E. The AUC metric is inherently unreliable and should not be used for model evaluation.

Answer: A,B,C,D

Explanation:
A, B, C, and D are all valid reasons for performance degradation in production. Sampling bias (A) means the training/validation data doesn't accurately reflect the production data. Temporal bias (B) arises when customer behavior changes over time. Overfitting (C) leads to good performance on the training/validation set but poor generalization to new data. Missing data (D) can negatively impact the model's ability to make accurate predictions. AUC is a reliable metric, especially when combined with other metrics, so E is incorrect.

NEW QUESTION # 107
A data scientist is analyzing website conversion rates for an e-commerce platform. They want to estimate the true conversion rate with 95% confidence. They have collected data on 10,000 website visitors, and found that 500 of them made a purchase. Given this information, and assuming a normal approximation for the binomial distribution (appropriate due to the large sample size), which of the following Python code snippets using scipy correctly calculates the 95% confidence interval for the conversion rate? (Assume standard imports like 'import scipy.stats as St' and 'import numpy as np').

Answer: A,E

Explanation:
Options A and E are correct. Option A uses the 'scipy.stats.norm.intervar function correctly to compute the confidence interval for a proportion. Option E manually calculates the confidence interval using the standard error and the z-score for a 95% confidence level (approximately 1.96). Option B uses the t-distribution which is unnecessary for large sample sizes and is inappropriate here given the context. Option C is not the correct way to calculate the confidence interval for proportion using binomial distribution interval function, it calculates range of values in dataset, instead of confidence interval. Option D uses incorrect standard deviation.

NEW QUESTION # 108
A data science team is evaluating different methods for summarizing lengthy customer support tickets using Snowflake Cortex. The goal is to generate concise summaries that capture the key issues and resolutions. Which of the following approaches is/are appropriate for achieving this goal within Snowflake, considering the need for efficiency, cost-effectiveness, and scalability? (Select all that apply)

A. Using the 'SNOWFLAKE.ML.PREDICT' function with a summarization task-specific model provided by Snowflake Cortex, passing the full ticket text as input to generate a summary.
B. Calling the Snowflake Cortex 'COMPLETE' endpoint with a detailed prompt that instructs the model to summarize the support ticket, explicitly specifying the desired summary length and format.
C. Developing a Python UDF that leverages a pre-trained summarization model from a library like 'transformers' and deploying it in Snowflake. Managing the model loading and inference within the UDF.
D. Creating a custom summarization model using a transformer-based architecture like BART or T5, training it on a large dataset of support tickets and summaries within Snowflake using Snowpark ML, and then deploying this custom model for generating summaries via a UDF.
E. Employing a SQL-based approach using string manipulation functions and keyword extraction techniques to identify important sentences and concatenate them to form a summary.

Answer: A,B

Explanation:
Options A and D are the most appropriate approaches. Snowflake Cortex provides summarization task-specific models that are optimized for performance and cost-effectiveness within the Snowflake environment, Option A utilizes the task-specific model using Snowflake's SNOWFLAKE.ML.PREDICT function. Option D utilizes the 'COMPLETE endpoint. Option B is more complex and resource-intensive, as it requires training a custom model. Option C is less effective because it is hard to implement accurate summarization logic only with SQL. Option E introduces external dependencies and management complexities.

NEW QUESTION # 109
You are using Snowpark Python to process a large dataset of website user activity logs stored in a Snowflake table named 'WEB ACTIVITY'. The table contains columns such as 'USER ID', 'TIMESTAMP', 'PAGE URL', 'BROWSER', and 'IP ADDRESS'. You need to remove irrelevant data to improve model performance. Which of the following actions, either alone or in combination, would be the MOST effective for removing irrelevant data for a model predicting user conversion rates, and which Snowpark Python code snippets demonstrate these actions? Assume that conversion depends on page interaction and a model will only leverage session id and session duration.

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

Answer: B

Explanation:
Option C is the most effective for this scenario. Focusing on sessions and their durations provides a more meaningful feature for predicting conversion rates. Removing bot traffic (A) might be a useful preprocessing step but doesn't fundamentally address session-level relevance. Option B's logic is flawed removing all Internet Explorer traffic isn't inherently removing irrelevant data. Option D oversimplifies the data, losing valuable information about user behavior within sessions. Option E introduces bias by randomly sampling and removing potentially important patterns, plus it is too simplistic. The code example in C demonstrates how to calculate session duration using Snowpark functions, join the filtered session data back to the original data, and then drop the irrelevant columns.

NEW QUESTION # 110
You're building a regression model using Snowpark Python to predict house prices. After initial training, you observe that the model consistently overestimates the prices of high-value houses and underestimates the prices of low-value houses. Given the options below, which optimization metric, along with code snippet to calculate it using Snowpark, would be most effective in addressing this specific issue?

A. Mean Absolute Error MAE - as it is sensitive to outliers and will penalize large errors more heavily.
B. Mean Squared Error (MSE) - as it is less sensitive to outliers than RMSE.
C. Root Mean Squared Error (RMSE) - as it gives more weight to larger errors, making it suitable for addressing the underestimation/overestimation problem.
D. Adjusted R-squared - as it penalizes the addition of irrelevant features, improving the model's generalization ability.
E. R-squared - as it measures the proportion of variance explained, directly addressing how well the model fits the data across all price ranges.

Answer: C

Explanation:
RMSE is the most effective metric in this scenario. Since the model consistently underestimates low values and overestimates high values, larger errors (the difference between predicted and actual prices) are occurring in these ranges. RMSE penalizes larger errors more heavily than MAE, making it more sensitive to these discrepancies and driving the model to improve its predictions for both high and low-value houses. The code snippet demonstrates how to calculate RMSE using Snowpark Python.

NEW QUESTION # 111
......

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PASS GUARANTEED 2025 HIGH PASS-RATE DSA-C03: SNOWPRO ADVANCED: DATA SCIENTIST CERTIFICATION EXAM RELIABLE TEST PATTERN

Pass Guaranteed 2025 High Pass-Rate DSA-C03: SnowPro Advanced: Data Scientist Certification Exam Reliable Test Pattern