Snowflake SPS-C01 시험 개요:

Snowflake SPS-C01 시험 요강 주제:

최신 Snowflake Certification SPS-C01 무료샘플문제

문제 #1
You have a complex Snowpark Python UDF that aggregates data from various sources and returns a dictionary containing several metrics (e.g., '{'average price': 12.50, 'total sales': 1000, 'customer count': 50}'). You need to operationalize this UDF and ensure proper data type handling for each metric. Which of the following is the MOST appropriate way to define the return type using the registration API?

A. Use a single 'VariantType' to represent the entire dictionary.
B. Use a single 'ArrayType' to represent the entire dictionary. 'Integer Type').
C. Define a 'StructType' with ' StructFielcf for each metric, specifying the appropriate data type (e.g.,
D. Define the return type as 'StringType' and serialize the dictionary to JSON within the UDF.
E. Use a 'MapType' with 'StringType' as the key type and 'VariantType' as the value type.

문제 #2
You are developing a Snowpark application to analyze customer data'. You need to create a Snowpark DataFrame from a list of dictionaries, where each dictionary represents a customer with 'id', 'name', and 'city' keys. The data should be loaded efficiently. Consider these scenarios: 1 . The input data can sometimes contain missing values (e.g., a customer might not have a city specified). 2. You want to ensure optimal performance when loading the data, as the list can be very large. 3. You need the resulting DataFrame's schema to correctly infer the datatypes based on the input dictionary's values. Which of the following methods and considerations should be used to create a Snowpark DataFrame from a list of dictionaries to meet these requirements?

A. Use 'session.createDataFrame(data, where 'schema' is explicitly defined to handle missing data and ensure correct data types. This improves performance over schema inference.
B. Leverage Snowpark's optimized data loading by converting the list of dictionaries to a Pandas DataFrame first and then create a Snowpark DataFrame using 'session.createDataFrame(pandas_df)'. Pandas has optimized data loading.
C. Use 'session.createDataFrame(data)' and then explicitly cast columns with potential missing values to the correct datatype using method to ensure 'NULL' handling.
D. Use 'session.createDataFrame(data)' with default settings. Snowflake will automatically infer the schema and handle missing values as 'NULL'.
E. Use 'session.createDataFrame(data, and specify the 'nullable' property for each field within the schema. Additionally, define the data type explicitly. This optimizes performance.

문제 #3
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?

A.

B.

C.

D.

E.

문제 #4
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. Leveraging Snowflake's parameter hierarchy by setting account-level parameters and inheriting them into the Snowpark session.
B. Using the 'snowsqr CLI tool to pre-configure session parameters before running the Snowpark application.
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.

문제 #5
You have a Snowpark DataFrame named 'products_df' with columns 'product_id' (INT), 'product_name' (VARCHAR), and 'price' (FLOAT). You want to create a new DataFrame called 'discounted_products df that includes all columns from 'products_df' plus a new column named 'discounted_price', which is calculated as the original price minus a discount percentage specified by the variable 'discount_rate' (e.g., 0.1 for 10%). The 'discount_rate' is stored in the database table named 'discount_table'. You want to load the rate to variable. Choose the correct ways to achieve this. (Select all that apply)

A.

B.

C.

D.

E.

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