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Snowflake SPS-C01 Exam Syllabus Topics:

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

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Snowflake Certified SnowPro Specialty - Snowpark 認定 SPS-C01 試験問題 (Q370-Q375):

質問 # 370
You are tasked with creating a secure UDF in Snowflake using Snowpark Python API that encrypts sensitive customer data before storing it. The UDF should be accessible only to users with specific roles. You have the following code snippet. What needs to be done to make it secure and operationalize for multiple users?

Considering Security best practices, what steps are critical for securing the UDF and granting appropriate permissions to other users?

A. Bypass role-based access control and use shared credentials for UDF execution.
B. Store the encryption key directly in the UDF code for simplicity.
C. Grant global USAGE privilege on the function's stage location to all users.
D. Use Snowflake's Secret object to securely store the encryption key and retrieve it within the UDF. Grant USAGE privilege on the secret to specific roles.
E. Grant EXECUTE privilege on the UDF and USAGE privilege on the database and schema containing the UDF to the roles needing access.

正解：D、E

解説：
Storing the encryption key directly in the UDF code is a major security vulnerability. Snowflake's Secret object provides a secure way to store and manage sensitive information like encryption keys. Granting EXECUTE privilege on the UDF and USAGE privilege on the database and schema is essential for allowing authorized users to execute the UDF. USAGE privilege on the database and schema allows users to 'see' the UDF. Option C is incorrect because granting global USAGE privilege on the stage location is overly permissive. Option E is incorrect as bypassing role-based access control is insecure.

質問 # 371
You have a Snowpark DataFrame 'df containing customer data with columns 'customer_id', 'signup_date' (TIMESTAMP NTZ), and 'country'. You need to create a new DataFrame that calculates the number of days since each customer signed up, but only for customers in 'USA' and 'Canada'. Furthermore, you want to filter out records where the signup was more than 365 days ago. Which of the following Snowpark code snippets will achieve this most efficiently?

正解：D

解説：
Option E is the most efficient. It first filters the DataFrame by country using 'isin' , which is optimized for multiple values. Then, it calculates 'days_since_signup' using 'datediff and finally filters based on the number of days. Option A is correct but not as efficient as using 'isin-. Option B calculates 'days_since_signup' before filtering, which is less efficient. Option C uses 'to_number' which would result in the difference being represented in milliseconds and would require further conversion. Also using 'to_number' may lead to data loss. Option D has incorrect operator precedence in the 'where' clause, making it functionally wrong.

質問 # 372
You are developing a Snowpark application that involves creating a set of stored procedures and UDFs to process data'. To ensure proper version control and dependency management, you decide to package your Python code into a single Python Wheel file and deploy it to Snowflake. Which of the following methods are valid for deploying and utilizing this Python Wheel file within Snowflake, considering best practices for maintainability and security? (Select TWO)

A. Upload the Python Wheel file to an external stage (e.g., AWS S3) and configure Snowflake to access the external stage, then reference the wheel file path in the USING' clause of the UDF or stored procedure.
B. Use the 'snowflake-cli' to push the Python Wheel file as a package, then add the package name to the list of packages when creating the stored procedure or UDF.
C. Upload the Python Wheel file to an internal stage and directly reference it in the UDF or stored procedure definition using the 'USING' clause.
D. Create a Conda environment file (environment.yml) that specifies the Python Wheel file as a dependency and use this file to create a Snowflake environment. Then, associate the stored procedures and UDFs with that environment.
E. Use the Snowsight UI to upload the Python Wheel file as a dependency for the Snowflake environment, making it available for all stored procedures and UDFs within that environment.

正解：B、D

解説：
Options D and E are the correct answers. Creating a Conda environment file and deploying it to Snowflake (Option D) allows for explicit version control and dependency management, ensuring consistent execution across environments. You need to upload the environment.yml which contains the packages and custom wheel you need to add. Using 'snowflake-cli' to push wheel as a package (Option E) is the approach for using custom packages. These are the recommended approaches. Uploading wheel files to internal or external stages and referencing them using the 'USING' clause (Options A and B) might work, but it lacks the structured dependency management provided by Conda environments. Snowsight cannot be used to directly upload wheels for environment setup (Option C).

質問 # 373
You are developing a Snowpark stored procedure in Python to perform sentiment analysis on customer reviews. The procedure relies on a custom Python library, 'sentiment_analyzer.py' , which is not available in Snowflake's default Anaconda channel. You also need to include the 'nltk' library. Which of the following approaches is the MOST efficient and recommended way to make both dependencies available to your stored procedure within Snowflake?

A. Upload 'sentiment_analyzer.py' and 'nltk"s compiled code as separate stages, then import them within the stored procedure using 'sys.path.append()'.
B. Install 'sentiment_analyzer.py' and 'nltk' on each Snowflake virtual warehouse node and set the 'PYTHONPATH' environment variable. (This will require contacting Snowflake support.)
C. Create a Snowflake Anaconda channel package containing 'sentiment_analyzer.pV and 'nltk' using 'conda build' , then reference this package in your stored procedure's 'imports' parameter.
D. Include the code from 'sentiment_analyzer.py' directly within the stored procedure's Python code and download 'nltk' modules from the internet each time the stored procedure is executed.
E. Create a ZIP file containing 'sentiment_analyzer.py' and the required 'nltk' modules, upload it to a stage, and specify the stage path in the 'imports' parameter of the 'sproc' decorator.

正解：E

解説：
Option C is the most efficient and recommended approach. Snowflake allows importing dependencies from a stage as a ZIP file. This avoids the complexity of creating a custom Anaconda package (Option B) or manually managing dependencies on each virtual warehouse node (Option D), which is not supported. Directly including the code (Option E) makes the procedure large and difficult to manage. Using (Option A) is generally discouraged as it's less robust for dependency management in Snowpark stored procedures.

質問 # 374
You have a Snowflake view named 'SALES SUMMARY VW' that joins several large tables and performs complex aggregations. You need to create a Snowpark DataFrame from this view Which of the following considerations are MOST important to ensure optimal performance and resource utilization when working with this DataFrame?

A. Use 'session.sql()' with the view's definition to create the DataFrame, as this is generally faster than 'session.table()' for views.
B. Avoid calling 'collect()' or 'toPandas()' on the entire DataFrame unless absolutely necessary, as these operations bring all data into the client's memory.
C. Leverage Snowpark's lazy evaluation and pushdown optimization capabilities by performing transformations and filtering on the DataFrame before executing actions.
D. Materialize the view into a temporary table using 'CREATE TEMPORARY TABLE AS SELECT FROM SALES SUMMARY VW before creating the Snowpark DataFrame.
E. Always specify the schema explicitly when creating the DataFrame from the view to prevent Snowpark from inferring it.

正解：B、C

解説：
Options A and C are crucial. Option A highlights the danger of bringing large datasets into the client environment. Option C emphasizes the importance of Snowpark's core principles for efficiency. Option B is incorrect; Snowpark can efficiently infer the schema. Option D is generally less optimal than using 'session.table()' directly for views because 'session.table()' provides more opportunities for optimization by Snowpark. Option E materializes the view which would consume storage and may not be ideal unless intermediate results needs to be re-used multiple times. Furthermore temporary tables are only viable in current session context

質問 # 375
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