SPS-C01はSnowflake Certified SnowPro Specialty - Snowparkの中でも難易度が高いことで知られ、出題範囲の広さに戸惑う受験者も少なくありません。CertShikenでは2026年最新の出題傾向を反映した374問の問題集を用意しています。

Snowflake SPS-C01 試験概要:

認定ベンダー:	Snowflake
試験名:	SnowPro® Specialty: Snowpark 認定試験
試験番号:	SPS-C01
合格点:	750点（スケーリング評価：0～1000点）
出題数:	55問
試験時間:	85 分
受験料:	375米ドル
対応言語:	英語
認定の有効期間:	2年間
関連資格:	SnowPro Specialty: Gen AI SnowPro Specialty: Native Apps SnowPro Core
試験形式:	複数選択式問題, シナリオ設定型問題, 単一選択式問題
推奨トレーニング:	Snowflake Snowpark トレーニングプログラム
受験申し込み:	Snowflake Certification Portal
サンプル問題:
受験方法:	オンライン監督付き受験または試験会場での受験
前提条件:	推奨条件：SnowPro Core 認定資格の取得、1年以上のSnowpark実務経験、Python/Java/Scalaの実務的なスキルを有すること
公式シラバスのURL:	https://learn.snowflake.com/en/certifications/snowpro-snowpark/

Snowflake SPS-C01 試験シラバストピック:

SPS-C01試験の疑問を解決するFAQ

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

問題 #1

You have a Snowpark application that processes sensor data from a large number of devices. You've implemented a scalar UDF in Python to calculate a complex statistical metric for each sensor reading. Initial tests show poor performance. Which of the following strategies would be MOST effective in improving the performance of this application, considering the nature of the computation and the data volume?

A. Implement caching for the UDF results, assuming the statistical metric calculations are frequently repeated for the same sensor readings.
B. Replace the Python scalar UDF with a Java scalar UDF, as Java typically offers better performance than Python in Snowflake.
C. Increase the warehouse size significantly to provide more resources for the scalar UDF execution.
D. Replace the Python scalar UDF with a Python vectorized UDF, ensuring the UDF processes data in batches instead of row-by-row.
E. Rewrite the UDF logic directly in SQL, leveraging Snowflake's built-in functions for statistical calculations.

問題 #2

You are tasked with creating a Snowpark UDTF (User-Defined Table Function) in Python to process a large CSV file stored in a Snowflake stage. Each row in the CSV represents a transaction, and you need to parse each row and extract specific fields based on a complex set of rules. The UDTF should return a table with the extracted fields. Consider the following code snippet:

A. The UDTF will execute correctly and efficiently in Snowpark, correctly processing each row of the CSV and returning the extracted fields as a table.
B. The UDTF will fail because the 'yield' statement is being called after using 'return' in the processing block. Remove the yield statement as it is incompatible.
C. The UDTF will run, but it will be slow due to the use of pandas DataFrame operations within the UDTF. Consider optimizing the code to use Snowpark DataFrame operations instead.
D. The code will raise an error because the 'read_csvs function is not available within the Snowpark UDTF context. The input needs to be processed differently.
E. The UDTF will run but will not return any data since the code currently lacks a 'session' object properly initialized for Snowpark operations inside the handler. Ensure the handler method has the session parameter and uses it.

問題 #3

A UDTF (User-Defined Table Function) named 'split_sentences" takes a text string as input and returns a table with each row containing a single sentence from the input. You need to grant SELECT privilege on this UDTF to a specific role, 'DATA ANALYST'. Which of the following SQL statements will achieve this?

A. GRANT ALL PRIVILEGES ON FUNCTION TO ROLE DATA_ANALYST;
B. GRANT USAGE ON FUNCTION TO ROLE DATA_ANALYST;
C. GRANT EXECUTE ON FUNCTION TO ROLE DATA_ANALYST;
D. GRANT OWNERSHIP ON FUNCTION TO ROLE DATA ANALYST,
E. GRANT SELECT ON TABLE FUNCTION TO ROLE DATA_ANALYST;

問題 #4

You have a Snowpark DataFrame named 'employee_df in VS Code. You want to define a user-defined function (UDF) in Python that calculates the bonus for each employee based on their salary and performance rating. The UDF should take the salary (SALARY) and performance rating (PERFORMANCE RATING) as inputs and return the bonus amount. Assume you have already established a Snowpark session. Which code snippet accurately defines and registers a UDF named 'calculate_bonus' that can be applied to the 'employee_df DataFrame?

A.

B.

C.

D.

E.

問題 #5

You are developing a Snowpark application to process customer reviews. You need to use a third-party sentiment analysis library, 'SentimentAnalyzer', which is NOT available in the Anaconda repository. You have the library JAR file stored in an internal artifact repository accessible via HTTP. Which of the following steps are necessary to make this library available to your Snowpark session?

A. Upload the JAR file toa Snowflake stage and register it as a Java UDF using CREATE FUNCTION.
B. Upload the JAR file to a Snowflake stage. Then use 'session.add_import' to make the file available in your Snowpark session.
C. Use 'session.add_dependency('/path/to/SentimentAnalyzer.jar')' in your Snowpark Python code after uploading the JAR to an internal stage.
D. Configure the Snowflake account-level parameter to point to the HTTP location of the JAR file. Then use session.add_import' to use it.
E. Create a conda environment that includes the JAR, upload it to a stage, and use the environment in Snowpark.

解説：