SPS-C01시험준비공부 & SPS-C01최신업데이트버전덤프문제공부

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Snowflake SPS-C01 Exam Syllabus Topics:

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

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최신 Snowflake Certification SPS-C01 무료샘플문제 (Q11-Q16):

질문 # 11
You have a Snowpark Python UDF that performs sentiment analysis on customer reviews. The UDF relies on a pre-trained machine learning model stored as a file in a Snowflake stage. To enhance security, you want to create a secure UDF. Which of the following steps are necessary to achieve this?

A. Wrap the UDF creation in a stored procedure with 'EXECUTE AS CALLER to elevate privileges and ensure model access.
B. Grant USAGE privilege on the stage containing the model file to the SNOWFLAKE.DATA_GOVERNANCE role.
C. When creating the UDF, specify 'secure=True' in the 'CREATE FUNCTION' statement, and explicitly grant USAGE privilege on the stage containing the model file to the role that executes the UDF using 'GRANT USAGE ON STAGE TO ROLE
D. Ensure the function definition specifies a 'context' parameter to pass security context.
E. Grant READ privilege on the stage containing the model file to the role that owns the secure UDF.

정답：C,E

설명：
Secure UDFs require explicit grants to access resources. Granting READ privilege on the stage to the UDF owner ensures access during definition. 'secure=True' makes the UDF secure. 'USAGE ON STAGE must be granted to the role executing the UDF to allow it to read from the stage at runtime. 'SNOWFLAKE.DATA GOVERNANCE' role doesn't automatically grant access, and 'EXECUTE AS CALLER is not directly related to granting access to the model file. 'context' is not a standard parameter for UDF definitions and does not manage security context directly.

질문 # 12
A UDTF (User-Defined Table Function) named 'split_sentences" takes a text string as input and returns a table with each row containing a single sentence from the input. You need to grant SELECT privilege on this UDTF to a specific role, 'DATA ANALYST'. Which of the following SQL statements will achieve this?

A. GRANT EXECUTE ON FUNCTION TO ROLE DATA_ANALYST;
B. GRANT ALL PRIVILEGES ON FUNCTION TO ROLE DATA_ANALYST;
C. GRANT USAGE ON FUNCTION TO ROLE DATA_ANALYST;
D. GRANT SELECT ON TABLE FUNCTION TO ROLE DATA_ANALYST;
E. GRANT OWNERSHIP ON FUNCTION TO ROLE DATA ANALYST,

정답：D

설명：
Since UDTFs return a table, you need to grant the 'SELECT privilege on the table function to allow the role to query the results produced by the UDTF. The syntax is 'GRANT SELECT ON TABLE FUNCTION'. option A is wrong it's for UDE Options C and E provide too many privileges, potentially allowing unintended modifications. Option D is not the correct Privilege.

질문 # 13
You have two Snowpark DataFrames: containing customer information and 'orders_df containing order details. You need to merge these DataFrames based on the column to create a unified view. The 'customers_df may contain duplicate values. The contains recent orders. You want to use 'merge' with an 'UPDATE action based on 'customer id' and a 'WHEN NOT MATCHED BY TARGET action to insert new customer records from 'customers df into the 'orders df table that do not exist.

A. The 'merge' operation will succeed, processing each matching record sequentially. If more than one row matches it will process based on order in the dataframe
B. You must use on the source DataFrame ('customers_df) before using 'merge' to ensure only one matching row exists per customer.
C. The 'UPDATE action in 'merge' will update all matching rows in the target table based on the join condition, regardless of duplicates in the source.
D. The 'merge' operation will fail immediately upon encountering the first duplicate key during the update operation. Consider stage the source dataframe and use an appropriate dedup option with merge.
E. The merge operation cannot handle duplicate keys in the source DataFrame without pre-aggregation.

정답：E

설명：
Snowflake's 'MERGE statement, including the Snowpark DataFrame 'merge' functionality, requires unique matching keys on the source side when performing updates. Duplicates in the source can lead to unpredictable behavior and incorrect updates. While the operation might execute without an explicit error, the results will be unreliable. Pre-aggregation or deduplication of the source DataFrame is necessary before using 'merge' in this scenario to ensure each target row is updated correctly. This is crucial for data integrity and consistent results when performing DML operations with Snowpark. Although it may process without explicit error, it will process each matching record based on order in the source which can lead to unexpected results. Therefore, it's best practice to dedup or pre-aggregate.

질문 # 14
You are developing a Snowpark application that needs to connect to Snowflake using account identifiers. Your organization's Snowflake account is configured with federated authentication (Okta). Which of the following methods is the most secure and recommended way to establish a Snowpark session in this scenario, avoiding hardcoding credentials in your application and leveraging existing authentication mechanisms?

A. Create a dedicated Snowflake user with restricted permissions and use its username and password directly in the connection string.
B. Store the username and password in environment variables and retrieve them in your Snowpark application to establish the session.
C. Pass username and password directly in the connection properties along with the account identifier.
D. Use the connection parameter along with username and password directly in the connection properties.
E. Utilize Snowflake's support for OAuth and configure your application to acquire a token from Okta and use it to establish the Snowpark session using the 'authenticator parameter set to 'oauth'.

정답：E

설명：
Using OAuth with an external identity provider like Okta is the most secure and recommended method for federated authentication. It avoids storing credentials directly in the application and leverages the organization's existing authentication mechanisms. Options A, B, and D are less secure due to the risk of exposing credentials. Option E does not address the federated authenication requirment.

질문 # 15
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)

정답：A,B,D,E

설명：
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.

질문 # 16
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