다운로드 Snowflake : SPS-C01 질문 & 답 PDF & Test Software

You are using Snowpark Python to transform a large DataFrame containing customer transaction data'. You need to persist the resulting DataFrame as a new Snowflake table named 'CUSTOMER TRANSACTIONS AGGREGATED', replacing the existing table if it exists. You want to explicitly define the schema of the new table to ensure data types are correctly enforced. Which of the following code snippets achieves this most efficiently and correctly?

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You need to create a Snowpark DataFrame using a SQL query. The query requires a user-defined variable (e.g., a date for filtering records). What are the correct and recommended ways to safely pass this variable into the SQL query when creating the DataFrame using 'session.sql()' to prevent SQL injection vulnerabilities?

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You are using Snowpark Python to process a DataFrame containing customer data,. One of the columns, 'phone_number' , contains phone numbers in various formats (e.g., '123-456-7890', '(123) 456-7890', '1234567890'). You need to standardize these phone numbers to the format 'XXX-XXX-XXXX' using a User-Defined Function (UDF). You want to create a UDF called 'standardize_phone_number' that takes a string as input and returns the standardized phone number. Which of the following code snippets correctly defines and registers this UDF in Snowpark, and applies it to the 'phone_number' column of the 'customer df DataFrame? Assume a Snowflake session object called 'session' is already available.

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You have a Snowpark DataFrame 'df' containing customer data with columns 'customer id', 'name', 'age', and 'city'. You want to filter the DataFrame to include only customers from 'New York' who are older than 30, then extract the 'customer id' and 'name' into a Rows object, and finally print the 'name' of the first row in the Rows object. Which of the following code snippets correctly achieves this using Snowpark Python?

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You have a Snowflake table 'PRODUCT REVIEWS' with columns 'product_id', 'review_text', 'review_date', and 'sentiment_score'. You are using Snowpark to build a data pipeline that analyzes these reviews and identifies potentially malicious or spam reviews. Based on a machine learning model deployed as a Snowflake UDF, you have identified a set of 'product_id' values that are associated with malicious reviews, and are stored in a python list called 'malicious_product_ids'. You need to delete all rows from the 'PRODUCT REVIEWS' table where the 'product_id' is present in the list. Assume 'malicious_product_ids' contains thousands of product IDs. Which of the following approaches is the MOST efficient and scalable for deleting these records using Snowpark?

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