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Snowflake SPS-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Snowpark API and Development	30%	- Multi-language support 1. Java and Scala API basics 2. Environment setup and dependencies - Python API fundamentals 1. Column operations and functions 2. DataFrame creation from tables, views, SQL 3. Data persistence and writing results
Topic 2: Data Transformations and Operations	35%	- User-defined logic 1. UDFs, UDAFs, UDTFs 2. Stored procedures with Snowpark - DataFrame manipulation 1. Filtering, sorting, grouping, aggregation 2. Selection, projection, renaming, casting 3. Joins, unions, set operations - Advanced operations 1. Semi-structured data processing 2. Pivot and unpivot transformations 3. Window functions and analytics
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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Snowflake SPS-C01問題集無料、SPS-C01復習内容

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Snowflake Certified SnowPro Specialty - Snowpark 認定 SPS-C01 試験問題 (Q55-Q60):

質問 # 55
When creating UDFs/UDTFs in Snowpark Python, what are the advantages of explicitly specifying data types (either via Python type hints or the registration API) compared to relying on implicit type inference?

A. Automatic data type conversion by Snowflake, eliminating the need for explicit casting within the UDF/UDTF.
B. Reduced deployment time.
C. Improved performance due to reduced overhead in data type resolution at runtime.
D. Enhanced code readability and maintainability, making it easier to understand the expected data types.
E. Early detection of type-related errors during development, preventing runtime failures.

正解：C、D、E

解説：
Specifying data types explicitly offers several benefits. (A) Explicit data types allow Snowflake to optimize query execution by eliminating the need to infer types at runtime, resulting in improved performance. (B) Type hints and registration APIs enhance code readability and maintainability by clearly indicating the expected data types. (C) Explicit data types enable early detection of type-related errors during development, preventing unexpected runtime failures. (D) While Snowflake can perform some implicit conversions, explicit type declarations don't guarantee automatic conversion in all scenarios and manual casting might still be needed. (E) deployment time is not significantly affected.

質問 # 56
You are working with a Snowpark DataFrame containing website traffic data'. The DataFrame has columns like 'date' , 'page_url', and 'visit_count'. You need to calculate the cumulative sum of visit counts for each 'page_url' over time (i.e., ordered by 'date'). However, you only want to consider data from the last 30 days for each calculation. Which of the following Snowpark code snippets will correctly achieve this using window functions with a frame specification?

正解：B

解説：
Option B is correct because it uses 'rangeBetween(-30, 0)' which specifies a frame that includes all rows within a range of 30 days preceding the current row, based on the ordering defined by the "date" column. Since 'date' column is used for ordering, we need to use rangeBetween' and NOT 'rowsBetween' . Also, we need to use 'sf.sum()' for Snowflake functions'. If the column is of Timestamp type, 'rangeBetween' represents days only, and in case it's numeric type represents numeric interval. Option A calculates the cumulative sum from the beginning of time for each page URL. Option C doesn't correctly specify the end of the frame. Option D has incorrect syntax and may not work. Option E uses incorrect sum function as well, as it misses sf prefix.

質問 # 57
You have a Python function named 'process data' that performs data cleaning and transformation on a Pandas DataFrame. You want to convert this function into a Snowpark Python stored procedure to leverage Snowflake's compute resources. However, the 'process_data' function relies on several external Python libraries (e.g., 'pandas', 'numpy', 'scikit-learn') that are not pre-installed in the Snowflake environment. Which of the following approaches would ensure that these dependencies are available within the Snowpark stored procedure? Choose all that apply

A. Bundle the required libraries into a ZIP file and upload it to a Snowflake stage. Then, add the ZIP file to the 'imports' list during stored procedure creation, ensuring that the library paths are correctly referenced within the Python code.
B. Use session.custom_package to resolve dependencied
C. Specify the required packages in the 'packages' argument of the '@sproc' decorator or 'session.add_packageS method.
D. Create a custom Anaconda environment with the required packages and upload it to a Snowflake stage. Then, specify the stage location in the 'imports' argument of the 'CREATE PROCEDURE statement.
E. Include the necessary 'import' statements for the libraries within the stored procedure's code. Snowflake will automatically resolve and install the dependencies.

正解：A、C

解説：
Options B and D are the correct ways to handle external dependencies for Snowpark Python stored procedures. Option B: The packages argument of the '@sproc' decorator or 'session.add_packages' method is the most straightforward way to specify dependencies. Snowflake will automatically download and install these packages from its Anaconda channel. Option D: Bundling libraries into a ZIP file and uploading it to a stage is a valid approach when you need to use specific versions of libraries or libraries that are not available in the Snowflake Anaconda channel. However, it requires careful management of library paths within the Python code. Option A is incorrect. Snowflake does not automatically resolve and install dependencies based solely on 'import' statements. Option C is a more complex approach and is generally not necessary unless you have very specific requirements for package versions or custom packages. It's often easier to use the 'packages' argument for standard libraries. Option E : there is no session.custom_package in snowpark python API.

質問 # 58
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. Infer the schema explicitly before creating the Snowpark DataFrame using Pandas DataFrame column types. For the string columns, define them to be StringType().
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. First, replace all NA/NaN values in Pandas DataFrame with None, then create Snowpark DataFrame using 'session.createDataFrame(customer_datay.

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 # 59
A data engineering team wants to create a Snowpark stored procedure that takes a VARIANT column from a Snowflake table, parses a specific JSON element within each row, and returns a new DataFrame with the extracted data as a STRING column. The JSON structure is consistent across all rows. What is the MOST efficient and type-safe way to implement this, considering the need for performance and maintainability?

A. Define the input column as a generic 'object' type in Python, use Snowpark's 'get function with path navigation to extract the JSON element, and return the extracted data as a string using 'as_varchar'.
B. Use the 'get' function on the VARIANT column to extract the JSON element, use the 'as_varchar' function to cast the VARIANT value to a String value, and register the stored procedure with explicit 'return_type' and schema definition for enhanced type safety
C. Use Snowpark's 'get' function within the stored procedure to extract the JSON element, explicitly cast the extracted value to STRING using 'cast('string')' , and register the stored procedure with defining the output schema.
D. Define the input column using and use the operator to implicitly convert the extracted JSON element to a string, relying on Snowpark's type inference for the return type.
E. Use Python type hints for the input VARIANT column, extract the JSON element using string manipulation within the stored procedure, and return a DataFrame with the extracted data as a string.

正解：B

解説：
The most efficient and type-safe approach is Option E. Using 'get' function on the VARIANT column to extract the JSON element, use the 'as_varchar' function to cast the VARIANT value to a String value, and register the stored procedure with explicit 'return_type' and schema definition for enhanced type safety. Snowpark's 'get' function provides optimized JSON parsing capabilities. Explicit casting using 'as_varchar' ensures that the extracted data is properly converted to a string. Registering the stored procedure with and a schema definition enforces data type safety at the interface level, preventing potential runtime errors and improving maintainability. Options A and D are less efficient or may lead to runtime errors. Option B is less explicit and more error prone.

質問 # 60
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