SPS-C01試験の準備方法｜ハイパスレートのSPS-C01日本語学習内容試験｜正確的なSnowflake Certified SnowPro Specialty - Snowpark試験対策書

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Snowflake SPS-C01 Exam Syllabus Topics:

Section	Objectives
Testing, Debugging, and Deployment	- Production readiness 1. Deployment strategies 2. Debugging Snowpark applications
User Defined Functions and Stored Procedures	- Extending Snowpark with custom logic 1. Stored procedures in Snowpark 2. Python UDFs
DataFrame Operations and Data Processing	- Data transformation workflows 1. Joins and window functions 2. Filtering, selecting, and aggregations
Performance Optimization and Best Practices	- Efficient Snowpark execution 1. Resource utilization tuning 2. Pushdown optimization concepts
Snowpark Fundamentals	- Snowpark architecture and concepts 1. Snowpark APIs and supported languages 2. Snowflake execution model overview
Data Engineering with Snowpark	- Pipeline development 1. Integration with Snowflake data pipelines 2. Batch processing workflows

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Snowflake Certified SnowPro Specialty - Snowpark 認定 SPS-C01 試験問題 (Q83-Q88):

質問 # 83
You are tasked with operationalizing a Snowpark Python UDF for batch scoring of a large dataset. The UDF takes a set of feature columns and returns a prediction. You want to optimize performance and resource utilization. Select all the strategies that would effectively improve the operational efficiency and scalability of your UDF execution.

A. Always use a warehouse size of 'X-Large' or larger regardless of the data volume to guarantee sufficient resources for UDF execution.
B. Ensure that the Snowpark DataFrame being passed to the UDF is appropriately partitioned based on a relevant column (e.g., a geographical region) before invoking the UDF.
C. Utilize the 'vectorized' argument during UDF registration to enable batch processing of input data within the UDF.
D. Adjust the 'MAX BATCH SIZE parameter for the warehouse executing the UDF to the largest possible value to minimize overhead.
E. If the UDF performs external API calls, implement retry logic with exponential backoff to handle transient network errors gracefully.

正解：B、C、E

解説：
Partitioning the input DataFrame (A) allows Snowflake to distribute the UDF execution across multiple nodes, improving parallelism. The 'vectorized' argument (B) enables the UDF to process data in batches, reducing per-row overhead. Implementing retry logic (D) improves resilience when calling external APIs. is not configurable. Using a fixed 'X-Large' warehouse (E) is not cost-effective; right- sizing the warehouse based on workload is crucial.

質問 # 84
You have a Snowflake table named 'RAW EVENTS with a large number of events data, containing columns like 'EVENT ID', 'TIMESTAMP, 'USER ID, and 'EVENT_TYPE. The 'EVENT TYPE column contains string values representing different event categories. You want to create a Snowpark DataFrame, but due to the table's size, you only want to sample a small portion of the data for initial exploration and testing. Which of the following code snippets MOST accurately and efficiently creates a sampled Snowpark DataFrame named 'sampled_df containing approximately 1% of the rows from the 'RAW EVENTS table?

正解：A

解説：
Explanation:Option A provides the MOST accurate and efficient way to sample 1% of the data using , seed-42)'. The 'frae parameter directly specifies the fraction of rows to sample. 'seed' ensures consistent sampling across multiple runs. Option B is inefficient because it calculates the total count of rows before limiting, which scans the entire table. Option C, uses 'randomSplit' , but it splits the data into two new dataframes. Option D, utilizes Snowflake's SQL sampling feature via 'session.sqr , is a valid alternative, but less idiomatic when using Snowpark API directly. Option E, the parameter 'n' is deprecated and has been removed. 'sample' requires 'frae parameter.

質問 # 85
You are working with Snowpark DataFrames representing sales transactions. The 'transactions df DataFrame contains recent transactions, while the 'sales_table' in Snowflake holds the historical sales data'. You need to merge the new transactions into the 'sales table', but you want to track which rows were inserted, updated, or left unchanged by the 'merge' operation. How can you capture this information using Snowpark and persist it to a separate table?

A. The merge operation automatically creates a system table or view that logs the details of each row that was inserted, updated, or left unchanged.
B. After the merge, query the 'sales_table' and compare it to a copy of the 'sales_table' taken before the merge. Identify inserted rows as those present in the new version but not in the old, and updated rows as those with different values in specific columns between the two versions. This strategy should be implemented with the help of external functions and UDFs, as it is an expensive operation and should not be computed with normal SQL.
C. It's not possible to capture the merge operation details (inserted/updated/unchanged rows) directly using Snowpark alone. You would need to implement custom logic outside of Snowpark to compare the data before and after the merge.
D. You can use the 'returning' clause within the merge statement to retrieve the impacted rows and their change status (INSERTED, UPDATED) and then write this data to a tracking table using 'write.saveAsTable' .
E. You can combine the 'returning' clause along with a stored procedure to retrieve the inserted/updated rows with their status and then use the stored procedure to write to a different tracking table using insert statement.

正解：D

解説：
The 'returning' clause is a powerful feature of the 'merge' statement in Snowflake SQL. It allows you to capture the rows that were affected by the merge operation, along with details about the type of change (INSERTED, UPDATED, DELETED). In Snowpark, you can leverage this by including a 'returning' clause in your 'merge' statement and then use the returned DataFrame to write the data to a tracking table. This provides a direct and efficient way to monitor the impact of your merge operations. Therefore the correct answer is B.

質問 # 86
You have a complex data pipeline implemented using Snowpark Tasks in a Directed Acyclic Graph (DAG). One of the tasks, , depends on the successful completion of two parent tasks, and 'task B'. You need to implement error handling such that if 'task_R fails, 'task_C' should not be executed, but should still complete its execution regardless of status. If 'task B' fails, 'task_C' should not be executed. How do you configure the task dependencies and error handling in Snowflake to achieve this behavior?

正解：C

解説：
Setting to execute 'AFTER task_A, task_B' establishes the dependency. Configuring on both 'task_R and 'task_B' ensures that if either task fails, it will suspend itself and prevent 'task_C' from running. This achieves the required behavior. Option A is incorrect because setting the parameter on 'task_C' will not prevent the execution of 'task_C' if either 'task_R or failed, but only suspend it after it tries to execute and fails. Option B is not correct because Snowflake's default behavior does not inherently skip 'task_C' unless specifically configured through dependencies. Option C is overly complex and doesn't accurately reflect intended behaviour.Option E is overly complex and doesn't accurately reflect intended behaviour. Option D will ensure only if both 'task_A' and runs successfully then runs else it will suspend the further execution.

質問 # 87
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. Use 'session.add_dependency('/path/to/SentimentAnalyzer.jar')' in your Snowpark Python code after uploading the JAR to an internal stage.
B. Upload the JAR file to a Snowflake stage. Then use 'session.add_import' to make the file available in your Snowpark session.
C. Configure the Snowflake account-level parameter to point to the HTTP location of the JAR file. Then use session.add_import' to use it.
D. Upload the JAR file toa Snowflake stage and register it as a Java UDF using CREATE FUNCTION.
E. Create a conda environment that includes the JAR, upload it to a stage, and use the environment in Snowpark.

正解：B

解説：
The correct approach involves uploading the JAR file to a Snowflake stage and then using 'session.add_import' (or its Scala equivalent) to make it available within the Snowpark session's environment. Creating a UDF directly (A) isn't the correct way to use it within Snowpark DataFrame operations. 'session.add_dependency' (B) is incorrect. is generally used for Python packages, not arbitrary JAR files accessed via HTTP. Using conda and deploying is not required for simple cases (E).

質問 # 88
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2026年Tech4Examの最新SPS-C01 PDFダンプおよびSPS-C01試験エンジンの無料共有：https://drive.google.com/open?id=1SCknPIJGZsuP7Fg8FX7Hcr276KDiJTbE