DSA-C03 무료 덤프문제 온라인 액세스

You are building a machine learning model using Snowflake data to predict customer churn. Your dataset includes a 'CUSTOMER TYPE column with the following possible values: 'New', 'Returning', and 'VIP'. You need to perform one-hot encoding on this column. Which of the following Snowflake SQL queries correctly implements one-hot encoding for the 'CUSTOMER TYPE column, creating separate binary columns for each customer type ('IS NEW', 'IS RETURNING', 'IS VIP')?

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

A Data Scientist is designing a machine learning model to predict customer churn for a telecommunications company. They have access to various data sources, including call logs, billing information, customer demographics, and support tickets, all residing in separate Snowflake tables. The data scientist aims to minimize bias and ensure data quality during the data collection phase. Which of the following strategies would be MOST effective for collecting and preparing the data for model training?

• A.Randomly select a subset of data from each table to reduce computational complexity and speed up model training.
• B.Create a single, wide table by performing a series of INNER JOINs on all tables using customer ID as the primary key. Handle missing values by imputing with the mean for numerical columns and 'Unknown' for categorical columns.
• C.Perform exploratory data analysis (EDA) on each table to identify relevant features and potential biases. Use feature selection techniques to reduce dimensionality. Implement robust data validation checks to ensure data quality and consistency before joining the tables. Handle missing values strategically based on the specific column and its potential impact on the model.
• D.Directly use all available columns from each table without any preprocessing to avoid introducing bias.
• E.Use Snowflake's Data Marketplace to supplement the existing data with external datasets, regardless of their relevance to the churn prediction problem.

A data scientist needs to calculate the cumulative moving average of sales for each product in a table. The table contains columns: (INT), (DATE), and (NUMBER). The desired output should include the product_id', 'sale_date', and Which of the following Snowflake SQL statements correctly calculates the cumulative moving average for each product using window functions?

• A.SELECT product_id, sale_date, daily_sales, AVG(daily_sales) OVER (PARTITION BY product_id) AS cumulative_average FROM sales_by_day;
• B.SELECT product_id, sale_date, daily_sales, OVER (PARTITION BY product_id ORDER BY sale_date ASC ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS cumulative_average FROM
• C.SELECT product_id, sale_date, daily_sales, OVER (PARTITION BY product_id ORDER BY sale_date AS cumulative_average FROM sales_by_day;
• D.SELECT product_id, sale_date, daily_sales, AVG(daily_sales) OVER (ORDER BY sale_date ASC) AS cumulative_average FROM sales_by_day;
• E.SELECT product_id, sale_date, daily_sales, OVER (PARTITION BY product_id ORDER BY sale_date ASC) / OVER (PARTITION BY product_id ORDER BY sale_date ASC) AS cumulative_average FROM sales_by_day;

Which of the following statements are TRUE regarding the 'Data Understanding' and 'Data Preparation' steps within the Machine Learning lifecycle, specifically concerning handling data directly within Snowflake for a large, complex dataset?

• A.During Data Preparation, you should always prioritize creating a single, wide table containing all possible features to simplify the modeling process.
• B.Data Understanding primarily involves identifying potential data quality issues like missing values, outliers, and inconsistencies, and Snowflake features like 'QUALIFY and 'APPROX TOP can aid in this process.
• C.The 'Data Understanding' step is unnecessary when working with data stored in Snowflake because Snowflake automatically validates and cleans the data during ingestion.
• D.Data Preparation should always be performed outside of Snowflake using external tools to avoid impacting Snowflake performance.
• E.Data Preparation in Snowflake can involve feature engineering using SQL functions, creating aggregated features with window functions, and handling missing values using 'NVL' or 'COALESCE. Furthermore, Snowpark Python provides richer data manipulation using DataFrame APIs directly on Snowflake data.

You are deploying a fraud detection model hosted on a third-party ML platform and accessing it via an external function in Snowflake. The model API has a strict rate limit of 10 requests per second. To prevent exceeding this limit and ensure smooth operation, what strategies could you implement within Snowflake, considering performance and cost implications? Select all that apply.

• A.Scale up the Snowflake virtual warehouse to the largest size possible. This will allow for more concurrent requests without exceeding the rate limit.
• B.Implement a retry mechanism within the external function definition to handle API rate limit errors (e.g., HTTP 429 errors) using exponential backoff.
• C.Utilize Snowflake's built-in caching mechanism for the external function results. This reduces the number of calls to the external API for repeated input data.
• D.Implement a custom queueing system within Snowflake using temporary tables and stored procedures to batch requests and send them to the external function at a controlled rate.
• E.Implement a UDF (User-Defined Function) that sleeps for 0.1 seconds before each call to the external function. This guarantees a maximum rate of 10 requests per second.