SPS-C01オンライン試験 & SPS-C01試験番号

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Snowflake SPS-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Transformations and Operations	35%	- Advanced operations 1. Pivot and unpivot transformations 2. Window functions and analytics 3. Semi-structured data processing - User-defined logic 1. Stored procedures with Snowpark 2. UDFs, UDAFs, UDTFs - DataFrame manipulation 1. Selection, projection, renaming, casting 2. Joins, unions, set operations 3. Filtering, sorting, grouping, aggregation
Topic 2: Snowpark API and Development	30%	- Python API fundamentals 1. DataFrame creation from tables, views, SQL 2. Column operations and functions 3. Data persistence and writing results - Multi-language support 1. Java and Scala API basics 2. Environment setup and dependencies
Topic 3: Snowpark Concepts and Architecture	25%	- Snowpark architecture and execution model 1. Transformations vs actions 2. Client-side vs server-side processing 3. Lazy evaluation and DAG execution - Session management and connection 1. Authentication and connection settings 2. Create and configure Snowpark sessions
Topic 4: Performance and Best Practices	10%	- Security and governance 1. Data protection and compliance 2. Access control and permissions - Optimization techniques 1. Query pushdown and execution plans 2. Minimizing data movement 3. Caching and warehouse sizing

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SPS-C01試験番号、SPS-C01日本語版と英語版

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Snowflake Certified SnowPro Specialty - Snowpark 認定 SPS-C01 試験問題 (Q33-Q38):

質問 # 33
You have a Snowpark DataFrame with columns 'product_id', 'customer_id', and 'sale_amount'. Some values are negative, indicating returns, and others are null. You need to replace negative values with 0 and fill null values with the average 'sale_amount' for each 'product_id'. Which of the following approaches is the MOST efficient and correct way to achieve this using Snowpark?

正解：A

解説：
Option E first replaces negative values with 0. Then, it calculates the average sales amount per product and joins it back to the original DataFrame. Finally, it fills null values with the calculated average sales amount and drops the temporary column. This is efficient because it utilizes Snowpark's DataFrame operations. other options does not handle nulls or gives errors.

質問 # 34
You have a Snowpark Python stored procedure that performs complex data transformations. This stored procedure needs to read data from a large table ('TRANSACTIONS) and write the transformed data to another table PROCESSED TRANSACTIONS'). You want to optimize the performance of this stored procedure by leveraging Snowpark's features for parallel processing. Which of the following approaches can significantly improve the performance of the stored procedure, assuming sufficient warehouse resources are available?

A. Use Snowflake's standard SQL queries within the stored procedure to read and transform the data. Write the results to the 'PROCESSED TRANSACTIONS table using 'INSERT statements.
B. Read the entire 'TRANSACTIONS table into a Pandas DataFrame within the stored procedure and perform the transformations using Pandas functions. Then, write the transformed data back to the table using Snowpark's 'createDataFrame' and 'write' methods.
C. Use Snowpark's 'sprocs decorator with appropriate 'packages' and leverage the Snowpark DataFrame API with vectorized UDFs to transform the data. Use 'session.write_pandaS to write the Pandas DataFrame to the 'PROCESSED_TRANSACTIONS' table after the transformation.
D. Use the Snowpark DataFrame API to read the 'TRANSACTIONS' table and apply transformations using vectorized UDFs. Then, use the 'write' method to write the transformed data to the 'PROCESSED TRANSACTIONS' table.
E. Load the data from 'TRANSACTIONS' table into a temporary table within the stored procedure, then use standard SQL queries on the temporary table for transformations, finally using snowpark DataFrame API to write it back to the 'PROCESSED_TRANSACTIONS' table.

正解：D

解説：
Using Snowpark DataFrame API along with vectorized UDFs leverages Snowflake's distributed processing capabilities for parallel execution, greatly enhancing performance. Reading the entire table into a Pandas DataFrame (Option B) limits parallelism and can lead to memory issues with large datasets. While SQL queries (Option C) work, they don't fully leverage Snowpark's optimized data transfer and processing. Option D refers to 'session.write_pandas' which isn't accurate in the context of writing transformed Snowpark data to Snowflake tables within the stored procedure. Using a temporary table and standard SQL queries, while functional, doesn't harness the full potential of Snowpark's distributed execution engine as effectively as using the DataFrame API directly (Option E).

質問 # 35
You are developing a Snowpark application that uses a Python UDF to perform geocoding operations. This UDF relies on a third-party geocoding library and a large dataset of geographical data stored in a file named 'geodata.db'. The UDF needs to be operationalized with minimal latency. Which of the following strategies will result in the FASTEST execution of the UDF and optimal resource utilization?

A. Use an external function that calls a geocoding service over the internet. Store 'geodata.db' in an S3 bucket and access it from the external function. Call the external service whenever it requires it.
B. Package the geocoding library and 'geodata.db' file into a ZIP file. Upload the ZIP file to a Snowflake stage and reference it using 'imports' in the UDF definition. Use a virtual environment to manage package dependencies.
C. Create a custom Anaconda channel containing the geocoding library and 'geodata.db'. Configure the Snowflake account to use this channel. No need to use virtual environment.
D. Create a Java UDF that performs the geocoding using a Java geocoding library. Upload the JAR file and 'geodata.db' to a stage and reference them using the 'imports' clause. Java UDFs always perform faster than Python UDFs.
E. Package the geocoding library and 'geodata.db' file into a ZIP file. Upload the ZIP file to a Snowflake stage and reference it using 'imports' in the UDF definition. Ensure 'geodata.db' is loaded only once into memory per worker process using global variable and proper caching for subsequent UDF invocations. Use a virtual environment to manage package dependencies.

正解：E

解説：
Option E is the most efficient strategy. Packaging the library and data file in a ZIP, referencing it with 'imports' , and using a global variable with caching within the UDF minimizes latency by loading the data only once per worker. It also benefits from utilizing the parallel processing capabilities of Snowpark. Using Java UDF's (C) is less efficient, unless it is highly optimized since java conversion can happen and adds overhead . Relying on external geocoding services (D) introduces network latency and is not ideal for performance. While a custom Anaconda channel (B) can simplify dependency management, it does not address the issue of loading the large 'geodata.db' file efficiently. Option A addresses the dependency managment but performance is not addressed.

質問 # 36
You're working with Snowpark and want to load data from a Pandas DataFrame into a Snowpark DataFrame. The Pandas DataFrame, 'customer_data' , contains columns with mixed data types (integers, strings, dates). Some columns also contain NULL values. You need to ensure that the data types are correctly inferred by Snowpark, NULL values are handled appropriately, and the resulting Snowpark DataFrame 'snowpark_customers' can be used for further transformations. What is the best approach to achieve this with minimal code and maximum performance?

A. First, replace all NA/NaN values in Pandas DataFrame with None, then create Snowpark DataFrame using 'session.createDataFrame(customer_datay.
B. Use 'session.write_pandas' because its optimized for large pandas dataframe.
C. Explicitly define the schema with StructType and StructField, specifying the column names and data types based on the Pandas DataFrame, converting null values to Snowflake's null representation during DataFrame creation.
D. Use 'session.createDataFrame(customer_datay and rely on Snowpark to automatically infer the schema and handle NULL values implicitly. Convert any problematic columns after the Snowpark DataFrame is created.
E. Infer the schema explicitly before creating the Snowpark DataFrame using Pandas DataFrame column types. For the string columns, define them to be StringType().

正解：B

解説：
Relying on schema inference (option C) might not always guarantee the correct data types, especially with dates or mixed-type columns. Explicitly defining the schema (option B) can be verbose and error-prone. Replacing NA/NaN with None and using 'createDataFrame' (option D) is a functional approach, but might not be as performant as the optimized method of 'write_pandaS. Inferring the schema (Option A) might not be fully accurate. Using session.write_pandas leverages internal Snowflake optimizations for data transfer and type handling from Pandas to Snowpark, making it the most efficient.

質問 # 37
You are tasked with building a Snowpark function to perform an upsert operation on a Snowflake table using a DataFrame. The function should take the target table name, a staging DataFrame, a join key column, and a list of columns to update. The function needs to handle potential schema evolution (i.e., columns may be added or removed from either the target table or the staging DataFrame) gracefully without causing the entire upsert to fail. Which of the following approaches, or combinations of approaches, would best address this requirement?

A. Before the merge, create a temporary table with the exact schema of the target table, insert all the data from the DataFrame into it, and then use the temporary table as source for the merge. Handle the schema evolution with dynamic sql if required.
B. Before the 'merge' operation, use 'DataFrame.select' on the staging DataFrame to project only the columns that exist in the target table.
C. Dynamically generate the SQL 'MERGE' statement within the function, comparing the columns present in the target table and the staging DataFrame, and only including those columns that exist in both.
D. Rely on Snowflake's automatic schema detection during the 'merge' operation to automatically adapt to schema changes.
E. Use the 'exceptAll' to ensure that there are no schema evolution issues.

正解：B、C

解説：
Approaches A and D are the most suitable for handling schema evolution during an upsert operation. Approach A involves dynamically generating the SQL WERGE statement by inspecting the schemas of both the target table and the staging DataFrame. This ensures that only the common columns are included in the update and insert clauses, preventing errors due to missing columns. Approach D suggests projecting the staging DataFrame to only include the columns that exist in the target table using DataFrame.select' . This effectively harmonizes the schema of the staging data with the target table's schema, avoiding issues during the 'merge' operation. While Snowflake does have some schema evolution capabilities, explicitly handling it in the code provides more control and predictability.

質問 # 38
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