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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. A data scientist uses bootstrapping to estimate the sampling distribution of a statistic calculated from a dataset stored in Snowflake. They observe that the bootstrap distribution is significantly different from the original data distribution. Which of the following statements best describes the possible reasons for this difference, considering both the theoretical underpinnings of bootstrapping and potential limitations?

A) Bootstrapping is only appropriate for normally distributed data; if the original data is not normal, the bootstrap distribution will inevitably differ significantly.
B) Bootstrapping always provides accurate estimates of sampling distributions, any significant difference indicates an error in the code implementation.
C) The statistic being estimated is inherently unstable and has a high variance, causing the bootstrap distribution to be wider and potentially different in shape compared to the original data distribution. This is a normal outcome when dealing with such statistics.
D) The difference is unexpected; the bootstrap distribution should always closely resemble the original data distribution, regardless of the statistic being estimated.
E) The original sample may not be representative of the population, and the bootstrap procedure is simply amplifying the biases present in the original sample. Additionally, the statistic itself may be highly sensitive to outliers or specific data points, leading to a distorted bootstrap distribution.

2. You are tasked with building a predictive model in Snowflake to identify high-value customers based on their transaction history. The 'CUSTOMER_TRANSACTIONS table contains a 'TRANSACTION_AMOUNT column. You need to binarize this column, categorizing transactions as 'High Value' if the amount is above a dynamically calculated threshold (the 90th percentile of transaction amounts) and 'Low Value' otherwise. Which of the following Snowflake SQL queries correctly achieves this binarization, leveraging window functions for threshold calculation and resulting in a 'CUSTOMER SEGMENT column?

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

3. You are analyzing customer churn for a telecommunications company. You have a Snowflake table called 'CUSTOMER ACTIVITY with columns 'CUSTOMER ID', 'CALL DURATION_SUM' (total call duration in minutes), 'DATA USAGE GB' (total data usage in GB), 'CONTRACT LENGTH MONTHS', and 'CHURNED' (boolean indicating whether the customer churned). You want to understand the relationship between these features and churn. Specifically, you want to visualize the distribution of 'CALL DURATION SUM' for churned and non-churned customers. Which of the following visualizations, combined with appropriate Snowflake SQL to prepare the data, would BEST illustrate the relationship between 'CALL DURATION SUM' and 'CHURNED'?

A) A pie chart showing the percentage of churned and non-churned customers, with no consideration of 'CALL DURATION SUM'
B) A line chart plotting the average 'CALL DURATION SUM' over time, ignoring the 'CHURNED' status.
C) A histogram of 'CALL DURATION SUM" for churned customers and a separate histogram of "CALL DURATION SUM' for non-churned customers, generated using an external visualization tool connected to Snowflake, after preparing the data using a CTE (Common Table Expression) in Snowflake to categorize customers by churn status.
D) A box plot with 'CHURNED on the x-axis and "CALL DURATION SUM' on the y-axis, generated using an external visualization tool connected to Snowflake, after preparing the data using a CTE (Common Table Expression) in Snowflake to categorize customers by churn status.
E) A scatter plot with on the x-axis and 'CHURNED' (0 or 1) on the y-axis, generated directly from the table using an external visualization tool connected to Snowflake.

4. 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').

A)

B)

C)

D)

E)

5. A financial institution suspects fraudulent activity based on unusual transaction patterns. They want to use association rule mining to identify relationships between different transaction attributes (e.g., transaction amount, location, time of day, merchant category code) that are indicative of fraud. The data is stored in a Snowflake table called 'TRANSACTIONS'. Which of the following considerations are CRITICAL when applying association rule mining in this fraud detection scenario?

A) Prioritize rules with high confidence and lift, even if support is relatively low, as rare but highly predictive combinations of attributes can be strong indicators of fraudulent activity.
B) Carefully discretize continuous variables like 'transaction amount' and 'time of day' into meaningful categories to enable association rule mining, and consider the impact of different discretization strategies on the resulting rules.
C) Focus solely on rules with very high support (e.g., > 0.1) to ensure statistical significance and avoid overfitting to rare fraudulent events.
D) Ensure that the Apriori algorithm is run directly within Snowflake using SQL to maximize performance and scalability, rather than extracting the data and processing it in an external Python environment.
E) Ignore transaction attributes that have a large number of distinct values (e.g., specific location coordinates) as they will likely lead to an explosion of rules and make interpretation difficult.

Solutions: