Snowflake SPS-C01日本語受験攻略、SPS-C01学習資料

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Snowflake SPS-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Data Transformations and DataFrame Operations	35%	- Filtering, Aggregating, and Joining DataFrames - Window functions - Complex data pipelines - Using built-in functions - Persisting transformed data
Snowpark Concepts	15%	- Stored procedures and conditional logic - Snowpark Sessions and connection management - Snowpark DataFrames and query plans - Client-side vs. Server-side execution - Snowpark architecture and core concepts - Transformations vs. Actions
Snowpark API for Python	30%	- DataFrame creation and manipulation - Working with Semi-structured data - Establishing connections and session management - Reading and writing data - User-Defined Functions (UDFs) and Stored Procedures
Performance Optimization and Best Practices	20%	- Vectorized UDFs - Caching strategies - Minimizing data transfer - Warehouse sizing for Snowpark - Debugging and explain plans - Query pushdown and optimization

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Snowflake Certified SnowPro Specialty - Snowpark 認定 SPS-C01 試験問題 (Q135-Q140):

質問 # 135
A data scientist is developing a Snowpark application that needs to authenticate to Snowflake using Key Pair Authentication. Which of the following steps are essential for configuring the Snowflake CLI to enable Key Pair Authentication and then correctly create a Snowpark session? (Select TWO)

A. Set the 'AUTHENTICATOR parameter in the Snowflake CLI configuration to 'EXTERNALBROWSER.
B. Set the 'AUTHENTICATOR parameter in the Snowflake CLI configuration to 'snowflake'.
C. Generate an RSA key pair using 'ssh-keygen' and store the private key securely.
D. Configure the user in Snowflake to use the public key by executing 'ALTER USER SET RSA_PUBLIC_KEY="'.
E. Set the 'PRIVATE KEY parameter in the Snowflake CLI configuration to the path of the private key file.

正解：C、D

解説：
Key Pair Authentication requires generating an RSA key pair (Option A) and configuring the Snowflake user with the public key (Option D). Setting the 'AUTHENTICATOR parameter to 'snowflake' is the correct approach. 'EXTERNALBROWSER uses the web-based browser authentication, while the private key is specified within the connection parameters directly, not the Snowflake CLI config.

質問 # 136
You have written a Snowpark Python function that performs a complex calculation involving user-defined functions (UDFs). When running this function on a large dataset, you encounter a 'PicklingError: Can't pickle ': it's not the same object as main.my function'. What is the MOST likely cause of this error, and how can you resolve it?

A. The dataset is too large to be processed in memory. Use 'df.cache()' to persist the intermediate results to disk.
B. The UDF's return type is not correctly specified. Use 'udf(func, to explicitly define the return type.
C. The UDF contains unsupported Python libraries. Ensure that all dependencies are available on the Snowflake worker nodes.
D. The Snowpark session is not properly initialized. Ensure that the connection parameters are correct.
E. The UDF is defined within a local scope or closure, and Snowpark cannot serialize it. Move the UDF definition to the global scope or use 'cloudpickle' explicitly.

正解：E

解説：
Pickling errors in Snowpark often arise when UDFs are defined within local scopes because the serialization process needs to transmit the function to the Snowflake worker nodes. Moving the UDF to the global scope or using 'cloudpickle' allows the function to be correctly serialized. Option B addresses memory issues, C handles dependency problems, D addresses connection issues, and E addresses return type issues, but these are not the MOST likely cause of a PicklingError related to function scope.

質問 # 137
You have a Snowflake table named 'raw events' with a VARIANT column named 'event data'. The 'event data' column contains JSON objects with a field 'timestamp' that is sometimes represented as a string and sometimes as a number (Unix epoch). You need to create a Snowpark DataFrame that extracts the 'timestamp' as a timestamp object, handling both string and numeric representations. Which of the following code snippets correctly accomplishes this, avoiding errors when encountering incompatible types?

正解：E

解説：
Option D correctly uses the 'is_number' function to check if the timestamp is numeric. If it is, it divides by 1000 (assuming milliseconds) and converts to a timestamp. If it's not numeric, it converts directly to a timestamp (assuming it is a string representation). Options A, B, C, and E will fail when encountering mixed data types because 'to_timestamp' expects either a number or a string, not both interchangeably. Casting numeric value as String then passing to 'to_timestamp' would raise issues, it needs to be divided.

質問 # 138
You are working with a data science team that needs to create Snowpark DataFrames from various file types (CSV, JSON, Parquet, and XML) stored in different locations (internal stages, external stages on AWS S3, and Azure Blob Storage). The team wants a unified and reusable function to create DataFrames, abstracting away the specific file format and location details. Which of the following approaches using Snowpark Python API will provide the MOST flexible and maintainable solution?

A. Use the 'session.sqr method with dynamically generated SQL queries that include the file format and location details. Construct the SQL query string based on the input parameters.
B. Implement a single function that uses a series of 'if/elif/else' statements to determine the file type and location, then calls the appropriate 'session.read' method with the corresponding options.
C. Create a generic function str, file_format: str, options: dicty that uses 'getattr(session.read, file format)' to dynamically call the appropriate 'session.read' method based on the 'file_format' parameter. Pass additional configuration through the 'options' dictionary.
D. Create a class hierarchy with an abstract base class 'DataFrameReader' that defines a 'read_file' method. Implement subclasses for each file format and location, overriding the 'read_file' method with the specific logic for that format and location.
E. Create separate functions for each file type and location combination (e.g.,

正解：C

解説：
Option C provides the best balance of flexibility, maintainability, and conciseness. Using 'getattr(session.read, file_format)' allows dynamically calling the appropriate 'session.read' method (e.g., 'session.read.csv', 'session.read.json') based on a string parameter. Passing additional configuration through a dictionary allows customizing the read operation without modifying the core function. Options A, B, D, and E are less flexible, more verbose, or less efficient.

質問 # 139
You have two Snowflake tables, 'customers' and 'orders'. The 'customers' table contains customer information, including a 'customer id' and 'region'. The 'orders' table contains order information, including 'order id', 'customer id', and 'order amount'. You need to create a Snowpark DataFrame that joins these two tables on 'customer id' and calculates the total order amount per region. However, some customers may not have any orders, and you want to include all customers in the result, with a total order amount of 0 for those without orders. Which of the following Snowpark code snippets will achieve this goal MOST efficiently, assuming 'customers_df and 'orders_ff are pre-existing Snowpark DataFrames representing the respective tables?

正解：B

解説：
Option B is the most efficient because it uses 'coalesce' directly within the 'agg' function, avoiding a separate .na.fill' operation which could be less optimized in Snowpark. It handles the null values resulting from the left outer join correctly, ensuring that customers without orders have a 0 total order amount. Options A and C might work in some contexts, but are less idiomatic and potentially less efficient. Options D and E are less concise and may not be the most optimal way to express the desired logic in Snowpark.

質問 # 140
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