SPS-C01資格復習テキスト & SPS-C01無料試験

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Snowflake SPS-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Performance Optimization and Best Practices	20%	- Caching strategies - Query pushdown and optimization - Debugging and explain plans - Warehouse sizing for Snowpark - Vectorized UDFs - Minimizing data transfer
Topic 2: Data Transformations and DataFrame Operations	35%	- Using built-in functions - Window functions - Persisting transformed data - Complex data pipelines - Filtering, Aggregating, and Joining DataFrames
Topic 3: Snowpark Concepts	15%	- Transformations vs. Actions - Snowpark DataFrames and query plans - Snowpark Sessions and connection management - Stored procedures and conditional logic - Snowpark architecture and core concepts - Client-side vs. Server-side execution
Topic 4: Snowpark API for Python	30%	- Reading and writing data - User-Defined Functions (UDFs) and Stored Procedures - Working with Semi-structured data - Establishing connections and session management - DataFrame creation and manipulation

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Snowflake Certified SnowPro Specialty - Snowpark 認定 SPS-C01 試験問題 (Q195-Q200):

質問 # 195
You are developing a data pipeline using Snowpark and want to optimize the execution of multiple DataFrame transformations. Which of the following strategies or techniques can you employ to improve performance and reduce execution time? (Select all that apply)

A. Leveraging Snowflake's caching mechanisms by using the 'CACHE RESULT clause after complex or frequently used queries.
B. Eagerly evaluating all DataFrame transformations using 'df.collect()' after each transformation to materialize the intermediate results.
C. Using pushdown optimization by writing UDFs in Scala and ensuring filter operations are applied as early as possible in the data processing pipeline.
D. Using on intermediate DataFrames that are reused multiple times in subsequent transformations.
E. Using to explicitly define the order in which DataFrames should be processed.

正解：C、D

解説：
Options C and E are correct. Option C, pushdown optimization by ensuring filter operations are applied as early as possible, is a key optimization technique. UDFs written in Scala can also be optimized by the compiler and Snowflake's engine. Option E, using , is the correct way to cache intermediate DataFrames for reuse, preventing redundant computations. Option A is incorrect; eagerly evaluating DataFrames with 'collect()' defeats the purpose of lazy evaluation and can significantly degrade performance. Option B is not directly applicable to Snowpark DataFrame transformations; 'CACHE RESULT is primarily for SQL queries executed outside of Snowpark DataFrame operations. Option D, is not a valid function in Snowpark API.

質問 # 196
Consider a DataFrame 'products df loaded from a SnoMlake table. It contains a 'features' column of type VARIANT, where each row contains a JSON object representing product features. Your task is to create a new DataFrame where each feature becomes a separate column. You need to dynamically extract these features without knowing the specific feature names in advance. Which of the following approaches could achieve this using Snowpark, and what considerations are important? Choose all that apply:

A. Use a User-Defined Function (UDF) to parse the JSON and return a dictionary. Then, use a loop to iterate over the keys in the dictionary and create new columns based on these keys.
B. The function can be used in conjunction with a Snowpark SQL query to dynamically extract the json into separate columns.
C. Use the 'FLATTEN' function within a Snowpark DataFrame transformation. This allows you to transform the key-value pairs within the VARIANT column into separate rows, which can then be pivoted to create new columns.
D. It's not possible to dynamically extract feature names and create columns in Snowpark without knowing the schema in advance.
E. Use the function on the VARIANT column to get an array of feature names. Then, use a loop to iterate over this array and dynamically create new columns using bracket notation (e.g.,

正解：C、E

解説：
Options B and C are viable approaches. Option B: You can use the native function on the VARIANT column to extract the keys, then iterate over the returned array to dynamically create new columns. This relies on knowing the structure of the data at runtime, but doesn't require a UDE Option C: FLATTEN' offers a SQL-centric way to achieve this, which might be preferable for performance and maintainability. After flattening, you would typically pivot the data. Option A is possible with IJDFs, but might be less performant than using native functions or FLATTEN. Option D is incorrect; dynamic column creation is possible. While OBJECT_CONSTRUCT() can construct JSON objects, it's not directly helpful for dynamically extracting JSON properties into separate columns in this scenario (Option E).

質問 # 197
You have a Snowpark DataFrame named 'employee_df with columns 'employee_id', 'department', and 'salary'. You want to calculate the average salary for each department and add it as a new column named 'avg_department_salary' to the original DataFrame. Additionally, you want to sort the resulting DataFrame by department and then by salary in descending order. Which of the following Snowpark code snippets correctly implements this requirement?

正解：A、B

解説：
Option D and E are correct. Both implement the window function correctly to add the average salary per department as a new column and then sort the DataFrame as required. Option E is very close and just has the window partition defined with 'col('department')'. Option A is correct but uses sort. Option B is incorrect because it does not include the original columns other than department when grouping so will loose important data, and attempts to sort on a column that no longer exists.Option C uses 'avg('salary')' instead of avg(col('salary'))

質問 # 198
A Snowpark developer is using to create a Snowpark session. They want to ensure that the session uses a specific role and warehouse, but only if those parameters are not already defined in the Snowflake CLI configuration. Which of the following code snippets correctly implements this behavior?

正解：E

解説：
Option D offers a concise method where role and warehouse specified in the 'Session.builder' are only used if they aren't already defined in the environment or Snowflake CLI configurations. Snowflake gives precedence to the environment over the code when using the session builder. Options A does not take into account how Session.builder.configs would work in order to read the current CLI configurations. Option B overwrites even though the question state that if these parameters are already defined in Snowflake CLI configurations they should NOT be changed. Option C does not have complete Code. Option E is overly complicated.

質問 # 199
You are developing a Snowpark Python application and want to write unit tests for your custom UDFs using pytest. Consider the following UDF:

Which of the following pytest tests are correctly written and effectively test the UDF, including handling potential exceptions? (Select all that apply)

正解：A、B、D、E

解説：
Options A, B, C, and D represent correctly written pytest tests. A tests a valid discount, B tests the exception handling for an invalid percentage, C tests the case with no discount, and D tests a full discount. E is incorrect because the calculate_discount function does not explicitly check for negative prices, therefore 'pytest.raises(ValueErrorV will cause a failed test since no ValueError is raised.

質問 # 200
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