Snowflake SPS-C01

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我々社はSnowflake Certified SnowPro Specialty - Snowpark勉強資料をリリースされる以来、たくさんの好評を博しました。試験に合格したお客様は「SPS-C01オンラインテストエンジンを利用して、模擬試験を繰り返して受けました。無事試験に合格しました。大変助かりました。」と感謝します。あなたの支持こそ我々は最も高品質のSnowflake Certified SnowPro Specialty - Snowpark問題集を開発して努力します。

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Snowflake Certified SnowPro Specialty - Snowpark 認定 SPS-C01 試験問題:

1. You are working with semi-structured data in Snowflake stored in a VARIANT column named 'payload'. You want to extract specific fields from this VARIANT column within a SQL query used to create a Snowpark DataFrame. Which of the following approaches allows you to access nested fields within the 'payload' column directly in the SQL query and create a corresponding column in your Snowpark DataFrame? Select all that apply.

A) First create a temporary table containing only the extracted fields using a separate SQL query, then create a Snowpark DataFrame from that table.
B) Use the "LATERAL FLATTEN(input payload)' function within the SQL query to unnest the VARIANT and then access the fields.
C) Use the 'payload:fieldl :field2 syntax directly within the SELECT statement in the SQL query.
D) Extract the VARIANT data into a Pandas DataFrame and then use Pandas to access the nested fields before creating the Snowpark DataFrame.
E) Use the 'fieldl .field2')' function within the SELECT statement in the SQL query.

2. You are developing a Snowpark application that needs to access data from a Snowflake table called 'EMPLOYEES. You want to create a Snowpark DataFrame representing this table. However, you are facing issues with the connection and believe that the database, schema, or warehouse attributes may not be set up correctly for the session. Which of the following code snippets, used in conjunction, BEST demonstrates how to create a Snowpark session with robust error handling to identify and address potential connection issues before attempting to create the DataFrame?

A)

B)

C)

D)

E)

3. A data engineering team is developing a Snowpark application to process large volumes of data'. They aim to leverage session parameters for fine-grained control over query execution and resource allocation. Which of the following methods is the MOST efficient and secure way to set session parameters, ensuring that sensitive information like warehouse size and query timeouts are dynamically adjusted based on the workload without hardcoding values in the application?

A) Using the 'snowsqr CLI tool to pre-configure session parameters before running the Snowpark application.
B) Leveraging Snowflake's parameter hierarchy by setting account-level parameters and inheriting them into the Snowpark session.
C) Directly using 'session.sql('ALTER SESSION SET QUERY _ TIMEOUT = for each session.
D) Utilizing Snowpark session builder to set parameters using a dictionary read from a secure configuration file, then overriding defaults based on workload characteristics. Example: 'session = Session.builder.configs(config).config('warehouse', workload_optimized_warehouse).create()'
E) Using environment variables to store parameter values and accessing them via 'os.environ['WAREHOUSE SIZET within the Snowpark application.

4. Consider a JSON structure representing product information, where prices are stored as strings due to inconsistent data quality. You need to calculate the average price of products. However, some price strings contain non-numeric characters (e.g., '$', commas). Which of the following approaches, using Snowpark DataFrame operations, is the MOST robust and efficient way to clean and cast the price data to a numeric type for accurate average calculation?

A)

B)

C)

D)

E)

5. You have a Snowpark DataFrame named 'orders df with columns 'order id', 'customer id', 'order date', and 'total amount'. You need to create a new DataFrame that contains only the 'customer_id" and the total number of orders placed by each customer. However, you want to perform this aggregation in parallel using a user-defined function (UDF) to improve performance. Which approach is MOST efficient and CORRECT?

A) Create a UDF that calculates the mode of total_amount, then apply the UDF to the DataFrame using 'select'.
B) Use 'groupBy' to group by 'customer_id' and then use the 'count' aggregate function. Do not use a UDF.
C) Create a UDF that performs the entire aggregation and call it with 'orders_df. The UDF uses a Pandas DataFrame internally to perform the count.
D) Create a UDF that takes an iterator of order IDs and returns the count. Apply this UDF to each partition of the DataFrame using 'maplnPartitions' .
E) Create a UDF that takes a customer ID as input and returns the count of orders for that customer, then apply this UDF to each distinct customer ID using 'map'.

質問と回答：