SPS-C01模擬試験サンプル、SPS-C01日本語認定

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Snowflake SPS-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Performance Optimization and Best Practices	20%	- Query pushdown and optimization - Debugging and explain plans - Vectorized UDFs - Warehouse sizing for Snowpark - Caching strategies - Minimizing data transfer
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
Snowpark Concepts	15%	- Client-side vs. Server-side execution - Transformations vs. Actions - Snowpark DataFrames and query plans - Snowpark Sessions and connection management - Stored procedures and conditional logic - Snowpark architecture and core concepts
Data Transformations and DataFrame Operations	35%	- Window functions - Filtering, Aggregating, and Joining DataFrames - Complex data pipelines - Persisting transformed data - Using built-in functions

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Snowflake Certified SnowPro Specialty - Snowpark 認定 SPS-C01 試験問題 (Q75-Q80):

質問 # 75
You have a Snowflake table 'PRODUCT CATALOG' with columns 'PRODUCT ID, 'PRODUCT NAME, and 'CATEGORY ID. You also have a table 'CATEGORY' with 'CATEGORY ID' and 'CATEGORY NAME. You need to create a Snowpark DataFrame that joins these two tables and includes only 'PRODUCT NAME and 'CATEGORY NAME. Assume a Snowpark Session object named 'session' is available. Which code snippet demonstrates creating the DataFrame using Snowpark's join functionality and column selection while using the 'table' method?

正解：E

解説：
Option D correctly joins the tables using the 'join' method with a column expression specifying the join condition. It also correctly selects only the desired columns using and option A incorrectly uses 'PRODUCT_ID instead of for the join and needs 'col()' to reference columns. option B selects 'PRODUCT_NAME', 'CATEGORY_NAME' without using 'col(Y which will cause an error since the join brings duplicate column names from product_df and category_df. Option C uses Pythonic '[['PRODUCT_NAME', 'CATEGORY_NAME'I]' which only works on Pandas dataframes. option E attempts to use col() to reference the columns in the join condition which is incorrect as it is a column expression needs to use fully qualified table name for the same named column in two dataframes; moreover selection is missing col().

質問 # 76
You are developing a Snowpark application that needs to train a machine learning model on a large dataset stored in Snowflake. You want to optimize the performance of your model training process. Which of the following strategies would be MOST effective in leveraging Snowpark's architecture to achieve this, considering both client-side and server-side capabilities?

A. Load the entire dataset into a Pandas DataFrame on the client machine and then use a local machine learning library (e.g., scikit-learn) to train the model.
B. Use Snowpark to execute SQL queries to pre-aggregate the data on the server-side before transferring the smaller aggregated data to the client for model training.
C. Create a UDF (User-Defined Function) that uses a pre-trained model stored locally on the client machine and applies it to the data within Snowflake.
D. Use Snowpark's 'sproc' feature to define a stored procedure that loads the data in chunks and trains the model directly within Snowflake's secure environment, leveraging its compute resources.
E. Define a Snowpark DataFrame that transforms the data, then write that data back to Snowflake as a new table, then create a separate Python process running outside of Snowflake's environment to train the model.

正解：D

解説：
Using a stored procedure ('sproc') allows the model training to occur entirely within the Snowflake environment, leveraging its scalable compute resources and eliminating the need to transfer large datasets to the client. This minimizes data transfer overhead and maximizes performance. Options A, D and E involve data transfer or execution outside the Snowflake environment which contradicts Snowpark's server-side processing advantages. Option C is a good preprocessing step, but doesn't train the model inside Snowflake.

質問 # 77
You are developing a Snowpark stored procedure in Python that utilizes the 'requests' library to fetch data from an external API. Your Snowflake account is configured to use Anaconda packages. You encounter an error indicating that the 'requests' library is not found. Which of the following steps are MOST effective in ensuring the 'requests' library is available to your stored procedure?

A. Enable Anaconda integration for your Snowflake account, ensuring 'requests' is available in the Snowflake Anaconda channel, and then create the stored procedure using 'imports=['snowflake://packages/requests/']'.
B. Install the 'requestS library directly onto the Snowflake compute nodes using SnowSQL's command.
C. Include the 'requests' library directly in the stored procedure code using a base64 encoded string.
D. Specify the 'requests library in the stored procedure's 'packages argument during creation: 'CREATE OR REPLACE PROCEDURE
E. Manually upload the 'requests' library's ' .py' files to an internal stage and import them within the stored procedure.

正解：D

解説：
The 'packages' argument in the 'CREATE OR REPLACE PROCEDURE statement is the correct way to specify dependencies on Anaconda packages. Snowflake will automatically resolve and make these packages available to the stored procedure. Option B is incomplete; while Anaconda integration is necessary, it doesn't automatically import the library. Options A, C and E are incorrect and not best practices.

質問 # 78
You are developing a Snowpark Python stored procedure for processing financial data'. The procedure uses the 'pandas' library for data manipulation and the 'scipy' library for statistical calculations. You want to optimize the execution of the stored procedure to leverage the available resources in your Snowflake environment. Which of the following strategies would be MOST effective in improving the performance of your stored procedure, considering the need to handle large datasets?

A. Convert the Snowpark DataFrame to a Pandas DataFrame within the stored procedure and perform all calculations using Pandas and SciPy.
B. Increase the warehouse size to the largest available option (e.g., X-Large) to provide more memory and CPU resources.
C. Utilize Snowpark's vectorized UDFs to perform the calculations on the data in parallel, avoiding the need to transfer the data to Pandas.
D. Use the 'cachetools' library within the stored procedure to cache intermediate results of calculations, reducing redundant computations.
E. Partition the input DataFrame into smaller chunks and process each chunk sequentially using Pandas, then combine the results.

正解：C

解説：
Option C is the most effective strategy for improving performance with large datasets. Snowpark's vectorized UDFs allow you to leverage Snowflake's distributed processing capabilities to perform calculations in parallel, avoiding the overhead of transferring data to Pandas. Option B would bring the entire dataset into memory on a single node which defeats the purpose of Snowflakes distributed computing. Option A might help but wouldn't fundamentally address the distribution issue. Option D is a brute-force approach and might help, but vectorized UDFs are more efficient. Option E could work but it requires complicated coding of partitioning logic.

質問 # 79
Which of the following statements are correct regarding account identifiers and their usage when creating Snowpark sessions in Python?

A. If the account identifier includes the region ID, you do not need to specify the region separately in the connection parameters.
B. The account identifier can be specified using either the 'Organization Name-Account Name' format or the legacy account locator, depending on the Snowflake account configuration and the region.
C. Using the 'Organization Name-Account Name' format for the account identifier is only valid for accounts that have been recently created.
D. Account identifiers are case-sensitive and must be entered exactly as provided by Snowflake.
E. Account identifiers can only be used when connecting to Snowflake accounts in the same AWS region.

正解：A、B

解説：
Account identifiers provide a unique way to connect to Snowflake accounts. If the identifier includes the region ID, specifying it separately is redundant. Snowflake supports both 'Organization Name-Account Name' and legacy account locators. Option A is incorrect because account identifiers are case-insensitive. Option B is incorrect, account identifiers can be used across regions. Option D is incorrect.

質問 # 80
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