SPS-C01模擬体験 & SPS-C01再テスト

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Snowflake SPS-C01 Exam Syllabus Topics:

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

>> SPS-C01模擬体験 <<

SPS-C01再テスト & SPS-C01最新試験情報

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Snowflake Certified SnowPro Specialty - Snowpark 認定 SPS-C01 試験問題 (Q264-Q269):

質問 # 264
A data engineer is tasked with creating a Snowpark session using JWT authentication. They have a private key 'rsa_key.pff, a user name 'snowpark_user' , and an account identifier 'my_account'. The goal is to create a session object suitable for submitting Snowpark jobs. Which code snippet correctly demonstrates the instantiation of a session object using JWT?

正解：B

解説：
Option B correctly reads the private key file, converts it to the required PEM format, and then uses it within the connection parameters to establish a Snowpark session. It handles the private key securely by loading and formatting it properly before passing it to the connection parameters. Option A, C and D attempts to directly provide the path to the private key or read the content with incorrect formatting which is incorrect. Option E doesn't address reading the private key in correct form and only address warehouse selection after the session is create.

質問 # 265
You have two Snowpark DataFrames: 'employees_df with columns 'employee_id' (INTEGER), 'employee_name' (STRING), 'department_id' (INTEGER), and 'salaries_df' with columns 'employee_id' (INTEGER), "salary' (FLOAT), 'effective_date' (DATE). You need to create a new DataFrame that contains the employee's name, department, and the highest salary they have ever received. Assuming there can be multiple salary entries for the same employee with different 'effective date' values, which of the following Snowpark code snippets would correctly and efficiently solve this problem?

正解：D

解説：
Option B is the most efficient solution. It first calculates the maximum salary for each employee in the DataFrame using 'groupBy' and 'max' , then joins this aggregated result with the 'employees_df to retrieve the employee's name and department. This approach avoids unnecessary data shuffling and minimizes the amount of data processed in the join. Option A performs the join before the aggregation, which can be less efficient. Options C and E use window functions, which are more complex and may not be as efficient for this simple aggregation. Option D uses a UDF and 'collect_list' , which can be very inefficient due to data transfer and UDF overhead.

質問 # 266
A Snowpark application is configured to connect to Snowflake using environment variables for authentication. However, the application frequently encounters intermittent connection errors. You suspect that the environment variables are not being correctly accessed by the Snowpark session builder. Which of the following actions could help diagnose and resolve the issue? (Select TWO)

A. Use to explicitly set connection parameters instead of relying on environment variables for debugging.
B. Verify that the user account specified in the environment variables has the necessary permissions (e.g., USAGE on the warehouse, database, and schema) in Snowflake.
C. Replace 'session.builder.getorcreate()' with 'Session.builder.appName('MySnowparkApp').create()'
D. Explicitly print the values of the relevant environment variables (e.g., 'SNOWFLAKE_USER , 'SNOWFLAKE_PASSWORD, 'SNOWFLAKE_ACCOUNT) within the Snowpark application before attempting to create the session.
E. Check that the environment variables are set at the correct scope (e.g., system-level, user-level, or within the application's execution environment) and are accessible to the Python process running the Snowpark application.

正解：D、E

解説：
Options A and E are the most helpful for diagnosing the issue. Printing the environment variable values (Option A) confirms that they are indeed set and have the expected values within the application's context. Checking the scope and accessibility of the environment variables (Option E) ensures that the Python process can access them. Option B, while a valid way to connect, defeats the purpose of using environment variables for security. Option C is also important, but less directly related to the environment variable issue; permissions are checked after connection. Option D does not help debug the environment variable issue, and the provided arguments are unrelated to environment variables.

質問 # 267
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 'PRIVATE KEY parameter in the Snowflake CLI configuration to the path of the private key file.
B. Generate an RSA key pair using 'ssh-keygen' and store the private key securely.
C. Configure the user in Snowflake to use the public key by executing 'ALTER USER SET RSA_PUBLIC_KEY="'.
D. Set the 'AUTHENTICATOR parameter in the Snowflake CLI configuration to 'EXTERNALBROWSER.
E. Set the 'AUTHENTICATOR parameter in the Snowflake CLI configuration to 'snowflake'.

正解：B、C

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 # 268
You're tasked with creating a Snowpark UDF to calculate the Haversine distance between two sets of latitude and longitude coordinates (point A and point B). Which of the following statements about deploying and using this UDF is/are TRUE?

A. When defining the UDF with input types, the Python types must exactly match the corresponding Snowflake data types.
B. The UDF, once defined, can be used inside of any DataFrame operation like 'select', 'filter' , and 'withColumn'
C. The UDF can be written in Python, Java, or Scala. Using a Java UDF will likely offer best performance, especially when dealing with very large datasets. You'll need to stage the compiled JAR file on an internal stage that Snowpark can access.
D. The UDF can only be called directly from within the Snowpark session and cannot be used in standard Snowflake SQL queries.
E. The UDF can only be written in Python and must be deployed as an inline UDF within the Snowpark session.

正解：B、D

質問 # 269
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