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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q148-Q153):
NEW QUESTION # 148
You are investigating website session durations stored in a Snowflake table named 'WEB SESSIONS. You suspect that bot traffic is artificially inflating the average session duration. You have the following session durations (in seconds) in the 'SESSION DURATION' column: [10, 12, 15, 18, 20, 22, 25, 28, 30, 1000]. Given this data and the context of bot traffic, which measure of central tendency is MOST robust to the influence of the outlier (1000) in this dataset? Assuming you already have table and dataframe created for this analysis. (Choose ONE)
Answer: E
Explanation:
The median is the most robust measure of central tendency in the presence of outliers. The mean is heavily influenced by extreme values. The mode is not guaranteed to be a stable measure. Geometric mean is also not robust. Trimmed mean can be useful, it's less robust compared to Median.
NEW QUESTION # 149
A data scientist is building a churn prediction model using Snowflake data'. They want to load a large dataset (50 million rows) from a Snowflake table 'customer_data' into a Pandas DataFrame for feature engineering. They are using the Snowflake Python connector. Given the code snippet below and considering performance and memory usage, which approach would be the most efficient for loading the data into the Pandas DataFrame? Assume you have a properly configured connection and cursor 'cur'. Furthermore, assume that the 'customer id' column is the primary key and uniquely identifies each customer. You are also aware that network bandwidth limitations exist within your environment. ```python import snowflake.connector import pandas as pd # Assume conn and cur are already initialized # conn = snowflake.connector.connect(...) # cur = conn.cursor() query = "SELECT FROM customer data```
Answer: A
Explanation:
Option E, utilizing Arrow result format and , is the most efficient for large datasets. Snowflake's Arrow integration leverages columnar data transfer, significantly speeding up data retrieval compared to row-based methods (fetchall, fetchmany). Also its optimized for Pandas. Options A, B, C, and D retrieve data row by row (or in chunks) and construct the DataFrame iteratively, which is slower and consumes more memory. The DictCursor in D, while useful, doesn't fundamentally change the data transfer efficiency compared to using the Arrow format.
NEW QUESTION # 150
You are building a fraud detection model using transaction data stored in Snowflake. The dataset includes features like transaction amount, merchant category, location, and time. Due to regulatory requirements, you need to ensure personally identifiable information (PII) is handled securely and compliantly during the data collection and preprocessing phases. Which of the following combinations of Snowflake features and techniques would be MOST suitable for achieving this goal?
Answer: A,B
Explanation:
Options A and E are the MOST suitable. Option A directly addresses PII protection by leveraging Snowflake's masking policies to redact sensitive data before it is used for model training. Role-based access control provides an additional layer of security by limiting access to the unmasked data. Option E applies differential privacy to protect individual transaction data while still enabling useful model training and combines it with Row Access policies to restrict access to sensitive transaction records. Option B is partially correct but insufficient, as it only addresses which columns are seen, not protection within those columns. Option C protects the entire database but doesn't address PII handling during model training. Option D is highly risky and non-compliant, as it exposes PII to a third party without adequate protection.
NEW QUESTION # 151
You are tasked with training a complex machine learning model using scikit-learn and need to leverage Snowflake's data for training outside of Snowflake using an external function. The training data resides in a Snowflake table named 'CUSTOMER DATA'. Due to data governance policies, you must ensure minimal data movement and secure communication. You choose to implement the external function using AWS Lambda'. Which of the following steps are crucial to achieve secure and efficient model training outside of Snowflake?
Answer: A,C,D
Explanation:
Options A, B, and D are correct. An API integration is essential for securely communicating with the external function via API Gateway, offering authentication and authorization. Granting usage privileges ensures only authorized roles can execute the external function. The external function serves as the bridge between Snowflake and the external service. Option C is incorrect because storing Snowflake credentials directly in Lambda environment variables is a security risk. Option E, while good practice, is not strictly related to the external function configuration for training model.
NEW QUESTION # 152
You are performing exploratory data analysis on a dataset containing customer transaction data in Snowflake. The dataset has a column named 'transaction_amount' and a column named 'customer_segment'. You want to analyze the distribution of transaction amounts for each customer segment using Snowflake's statistical functions. Which of the following approaches would BEST achieve this, providing insights into the central tendency and spread of the data?
Answer: E
Explanation:
Option E is the best approach. It uses to calculate the mean, to calculate the median (robust to outliers), to calculate the standard deviation (measure of spread), and 'QUANTILE(transaction_amount, 0.25, 0.5, 0.75)' to calculate the quartiles (25th, 50th, and 75th percentiles), all grouped by 'customer_segment'. This provides a comprehensive view of the distribution. Option A only provides an approximate count of distinct transaction amounts and the average. Option B provides standard deviation, variance, and median but lacks the mean and quartiles. Option C provides the range and count, which are useful but not as comprehensive. Option D calculates correlation and covariance, which are useful for understanding the relationship between transaction amount and customer segment (assuming customer segment is appropriately encoded numerically), but not for analyzing the distribution within each segment. It is important to note that 'QUANTILE' can also be accomplished using 'APPROX_PERCENTILE'
NEW QUESTION # 153
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