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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q21-Q26):

NEW QUESTION # 21
You are tasked with validating a regression model predicting customer lifetime value (CLTV). The model uses various customer attributes, including purchase history, demographics, and website activity, stored in a Snowflake table called 'CUSTOMER DATA. You want to assess the model's calibration specifically, whether the predicted CLTV values align with the actual observed CLTV values over time. Which of the following evaluation techniques would be MOST suitable for assessing the calibration of your CLTV regression model in Snowflake?

A. Evaluate the model's residuals by plotting them against the predicted values and checking for patterns or heteroscedasticity.
B. Conduct a Kolmogorov-Smirnov test to check the distribution of predicted and actual value.
C. Calculate the Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) on a hold-out test set to quantify the overall prediction accuracy.
D. Create a calibration curve (also known as a reliability diagram) by binning the predicted CLTV values, calculating the average predicted CLTV and the average actual CLTV within each bin, and plotting these averages against each other.
E. Calculate the R-squared score on a hold-out test set to assess the proportion of variance in the actual CLTV explained by the model.

Answer: D

Explanation:
Option B is the most suitable technique for assessing calibration. A calibration curve directly visualizes the relationship between predicted and actual values, allowing you to see if the model is systematically over- or under-predicting CLTV for different ranges of predicted values. Options A, C, and D are useful for assessing overall accuracy and model fit but do not directly address calibration. MAE and RMSE (A) measure overall error magnitude. Residual analysis (C) can reveal problems with model assumptions. R-squared (D) measures the explained variance, not calibration. Option E measures whether two samples follow the same distribution, however, it would not be most suitable for assessing calibration of your CLTV regression model.

NEW QUESTION # 22
A data science team is developing a churn prediction model using Snowpark Python. They have a feature engineering pipeline defined as a series of User Defined Functions (UDFs) that transform raw customer data stored in a Snowflake table named 'CUSTOMER DATA'. Due to the volume of data (billions of rows), they need to optimize UDF execution for performance. Which of the following strategies, when applied individually or in combination, will MOST effectively improve the performance of these UDFs within Snowpark?

A. Repartitioning the DataFrame by a key that distributes data evenly across nodes before applying the UDFs, using the method and minimizing data shuffling.
B. Leveraging external functions that call an API endpoint hosted on a cloud provider to perform data transformation. The API endpoint should utilize a serverless architecture.
C. Utilizing vectorized UDFs with NumPy data types wherever possible and carefully tuning batch sizes. Ensure that the input data is already sorted before passing to the UDF.
D. Using temporary tables to store intermediate results calculated by the UDFs instead of directly writing to the target table.
E. Converting Python UDFs to Java UDFs, compiling the Java code, and deploying as a JAR file in Snowflake. Using a larger warehouse size is always the best first option.

Answer: A,C

Explanation:
Vectorized UDFs (B) are optimized for performance by processing data in batches, significantly reducing the overhead associated with individual row processing. Repartitioning (E) ensures data is evenly distributed across nodes, allowing for parallel execution of UDFs and reducing skew, which can lead to performance bottlenecks. Java UDFs while faster than unoptimized Python UDFs, require extra work and maintenance, while vectorized UDFs are a more straight forward solution within Snowpark Python. Using temporary tables (D) could add overhead rather than reducing it. Using External functions (C) is a complex solution for what can be handled natively.

NEW QUESTION # 23
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: C,D

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 # 24
A data scientist is analyzing website traffic data stored in Snowflake. The data includes daily page views for different pages. The data scientist suspects that the variance of page views for a particular page, 'home', has significantly increased recently. Which of the following steps and Snowflake SQL queries could be used to identify a potential change in the variance of 'home' page views over time (e.g., comparing variance before and after a specific date)? Select all that apply.

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

Answer: A,B,C,D

Explanation:
Options B, C, D and E are correct. Option B directly compares the variance before and after a date, allowing for a direct assessment of change. Option C uses a window function for a rolling variance calculation, revealing trends over time. Option D creates a histogram, which helps visualize the distribution and identify shifts in spread. Option E calculates standard deviation before and after a date. Option A, while calculating the overall variance, doesn't provide insight into changes over time.

NEW QUESTION # 25
You are developing a real-time fraud detection system using Snowpark and deploying it as a Streamlit application connected to Snowflake. The system ingests transaction data continuously and applies a pre-trained machine learning model (stored as a binary file in Snowflake's internal stage) to score each transaction for fraud. You need to ensure the model loading process is efficient, and you're aiming to optimize performance by only loading the model once when the application starts, not for every single transaction. Which combination of approaches will BEST achieve this in a reliable and efficient manner, considering the Streamlit application's lifecycle and potential concurrency issues?

A. Load the model outside of the Streamlit application's execution context (e.g., in a separate script) and store it in a global variable. Access this global variable within the Streamlit application. This approach requires careful handling of concurrency.
B. Leverage the 'snowflake.snowpark.Session.read_file' to load the model binary directly into a Snowpark DataFrame and then convert to a Pandas DataFrame. Then, use the 'pickle' library for deserialization.
C. Load the model within a try-except block and set the Snowpark session as a singleton that will guarantee model loads once for the entire application.
D. Use the 'st.cache_data' decorator in Streamlit to cache the loaded model and Snowpark session. Load the model directly from the stage within the cached function. This approach handles concurrency and ensures the model is only loaded once per session.
E. Use Python's built-in 'threading.Lock' to serialize access to the model loading code and the Snowpark session, preventing concurrent access from multiple Streamlit user sessions. Store the loaded model in a module-level variable.

Answer: D

Explanation:
Option A is the best approach. 'st.cache_data' is the recommended way to cache data in Streamlit, including large objects like machine learning models. It automatically handles concurrency and ensures the model is only loaded once per Streamlit application instance. Because it's Streamlit's mechanism, it plays well with the Streamlit lifecycle. It is not required to use a Pandas DataFrame like option C suggests. Python global variables (B) are not suitable for web apps due to concurrency issues. While threading locks (D) could work, they add complexity and are generally less desirable than using Streamlit's caching mechanism. The model loading can be cached without a try-except block to set the Snowflake session as a singleton (E).

NEW QUESTION # 26
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New DSA-C03 Test Papers - Free PDF First-grade Snowflake DSA-C03 Reliable Test Answers

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q21-Q26):

New DSA-C03 Test Papers - Pass Guaranteed 2026 First-grade DSA-C03: SnowPro Advanced: Data Scientist Certification Exam Reliable Test Answers

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