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NEW QUESTION # 181
You are designing a data pipeline that requires applying a complex scoring algorithm to customer data in Snowflake. This algorithm involves multiple steps, including feature engineering, model loading, and prediction. You want to encapsulate this logic within a reusable component and apply it to incoming data streams efficiently. Which of the following approaches is most suitable and scalable for implementing this scoring logic as a UDF/UDTF, considering real-time data processing and low latency requirements?
Answer: B
Explanation:
Python UDTFs in Snowpark provide a powerful and scalable way to perform complex scoring algorithms, especially when combined with the GPU. Snowpark optimizes data processing within Snowflake's engine, and integration with Anaconda allows for using machine learning libraries such as scikit-learn or Pytorch for model loading and prediction and external libraries like 'torch' for accelerated calculations and ML model inference by GPU. SQL UDFs are not suitable for complex algorithms. JavaScript UDFs lack the necessary functionality and performance for advanced scoring. While Java UDTFs can be used, managing JAR files and potentially less efficient integration can be disadvantages. Using Python with SNOWPARK for GPU is suitable for real-time scoring and low latency.
NEW QUESTION # 182 
Answer: C
Explanation:
The most likely cause is (B). Snowflake expects the external function to return a single value directly convertible to the declared return type. The Lambda function is returning a JSON object that needs to be parsed. Snowflake needs a wrapper function to extract the numerical result from the json response. All other issues have been taken care of in the question and is not the cause of the problem.
NEW QUESTION # 183
Given the following scenario: You have an external table 'EXT SALES in Snowflake pointing to a data lake in Azure Blob Storage. The storage account network rules are configured to only allow specific IP addresses and virtual network subnets, enhancing security. You are getting intermittent errors when querying 'EXT SALES. Which of the following could be the cause(s) and the corresponding solution(s)? Select all that apply.
Answer: A,D
Explanation:
Options A and C are the most likely causes. Network restrictions often lead to connectivity issues if Snowflake's IP addresses are not whitelisted (A). Incorrect permissions for the Snowflake service principal (C) will also prevent access to the data lake. Option B is relevant if there are issues around schema changes or new files added. Option D will cause errors all the time not intermittently. Option E Snowflake automatically retries some queries. In a secure environment where IP whitelisting is mandated and IAM roles are properly configured, intermittent failures suggest a permission or a networking issue.
NEW QUESTION # 184
You are using Snowpark Python to perform data transformation on a large dataset stored in a Snowflake table named customer transactions'. This table contains columns such as 'customer id', 'transaction date', 'transaction amount', and product_category'. Your task is to identify customers who have made transactions in more than one product category within the last 30 days. Which of the following Snowpark Python snippets is the most efficient way to achieve this, minimizing data shuffling and maximizing query performance?
Answer: C
Explanation:
Option E is the most efficient. It filters the transactions within the last 30 days, uses a window function to calculate the distinct count of product categories per customer, and then filters to identify customers with more than one category. The use of window functions allows the aggregation to happen within Snowflake, minimizing data transfer and leveraging Snowflake's processing power. distinct at the end makes it unique.
NEW QUESTION # 185
You have a Snowpark DataFrame 'df_products' with columns 'product id', 'category', and 'price'. You need to perform the following transformations in a single, optimized query using Snowpark Python: 1. Filter for products in the 'Electronics' or 'Clothing' categories. 2. Group the filtered data by category. 3. Calculate the average price for each category. 4. Rename the aggregated column to 'average_price'. Which of the following code snippets demonstrates the most efficient way to achieve this?
Answer: A
Explanation:
Option B is the most efficient and correct. It uses 'col()' from 'snowflake.snowpark.functions' to properly reference the 'category' and 'price' columns, uses 'isin()' for a more concise and efficient filtering of multiple category values, groups by the category using and calculates the average price with 'avg(col('price')).as_('average_price')'. Option A, C, and D are syntactically incorrect or less efficient ways to accomplish the same task within Snowpark. Option E is incorrect because it utilizes 'to_pandas()' which returns the result as a Pandas DataFrame rather than a Snowpark DataFrame, failing to adhere to the Snowpark environment. While Option D is very similar, it lacks the proper syntax for specifying column references with 'col('category')' in the groupBy and 'col('price')' in the avg function.
NEW QUESTION # 186
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