최신NAS-C01최신인증시험공부자료인증시험대비자료

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Snowflake NAS-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Application Deployment & Distribution	25%	- Listing types and monetization - Versioning and release management - Upgrades and lifecycle management - Publishing to Snowflake Marketplace
Application Design & Creation	35%	- Security and access control - Setup scripts and application logic - Manifest files and configuration - Native App Framework architecture - Application packages and objects
Advanced Features & Management	20%	- Governance and compliance - Billing events and cost monitoring - Event logging and telemetry - Integration with Snowpark and external services
Application Installation & Testing	20%	- Installation procedures and dependencies - Debugging and troubleshooting - Provider and consumer workflows - Testing strategies and validation

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NAS-C01시험패스 가능한 공부자료 - NAS-C01인증시험대비 공부문제

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최신 SnowPro Core Certification NAS-C01 무료샘플문제 (Q184-Q189):

질문 # 184
You are developing a Snowflake Native Application that leverages Snowflake's Snowpark API for data processing. The application performs a series of complex transformations on a DataFrame. You need to optimize the application's performance and minimize resource consumption. Which of the following strategies would be MOST effective in achieving this goal?

A. Explicitly specify the execution order of the transformations using 'cache()' on each intermediate DataFrame to force immediate materialization.
B. Rely on Snowpark's query optimizer to automatically optimize the entire data processing pipeline without any manual intervention.
C. Break down the DataFrame processing into smaller steps and store the intermediate results in temporary tables within the application's sandbox database.
D. Use the method frequently to materialize intermediate DataFrames and inspect the data.
E. Employ lazy evaluation by chaining together transformations on the DataFrame and only triggering execution when the final result is needed.

정답：E

설명：
Lazy evaluation (C) is the most effective strategy. By chaining transformations and only executing when the final result is needed, Snowpark's query optimizer can optimize the entire pipeline, reducing resource consumption. 'collect()' (A) materializes DataFrames unnecessarily, hindering optimization. While Snowpark does optimize queries (B), relying solely on it is insufficient. Explicit materialization (D) defeats the purpose of lazy evaluation. Storing intermediate results in temporary tables (E) adds overhead and complexity.

질문 # 185
You are developing a Snowflake Native App that processes sensitive customer dat a. To comply with regulations, you need to ensure that data remains within the customer's Snowflake account and is not exposed to your provider account. Which of the following architectural approaches are MOST effective for achieving this goal?

A. Leverage external stages to store data outside of Snowflake, and use the app to access the data through the stage connection.
B. Employ data clean room functionality, leveraging agreed-upon procedures and technical controls to prevent inappropriate data disclosure.
C. Utilize external functions with secure data sharing, ensuring all processing logic resides in the consumer's account.
D. Use secure UDFs deployed within the consumer's account, allowing the app to process data directly without moving it.
E. Implement data masking policies within the provider account and share only the masked data with the consumer.

정답：B,C,D

설명：
Options A, C, and E directly address the requirement of keeping data within the customer's account. External functions (with secure data sharing) and secure UDFs allow processing within the consumer's environment. Data clean rooms provide a secure environment for collaboration and data analysis while minimizing data transfer. Option B violates the requirement by processing data in provider account. External stages (D) do not guarantee data residing within consumer's snowflake environment.

질문 # 186
You are developing a Snowflake Native Application that uses a custom Python handler to perform data transformations. This handler requires access to a third-party Python package not included in the standard Anaconda environment provided by Snowflake. To ensure the application functions correctly on consumer accounts, what steps must you take? (Select all that apply)

A. Use the SALTER FUNCTION' command after installation on the consumer's account to add the required Python package to the function definition. This option is not possible.
B. Instruct consumers to manually install the necessary Python package using 'pip install' in a Snowflake Snowpark Python worksheet after installing the application.
C. Create a stage and upload the python package(s). Then in the Python UDF code, reference the python package from the stage.
D. Specify the Python package as a dependency in the application's setup script using a install command.
E. Include the required Python package in the application package as a zip file using the 'CREATE or REPLACE FUNCTION' command with the 'imports' clause.

정답：C,E

설명：
Snowflake Native Applications cannot rely on consumers manually installing dependencies or executing arbitrary shell commands for security reasons. Including the Python package as a zip file via the 'imports' clause within the 'CREATE or REPLACE FUNCTION' command ensures that the dependency is automatically available when the Python handler is executed within the application environment on the consumer's account. Option C is not a valid command and not permissible in Snowflake. Also, consumers cannot use alter after the installation, Hence it will not work. A and D are the correct choices.

질문 # 187
Your Snowflake Native Application, 'Advanced Analytics Suite', utilizes dynamically generated SQL queries based on user input. To prevent SQL injection vulnerabilities when publishing this application, which of the following coding practices should you implement?

A. Sanitize all user inputs using Snowflake's built-in functions like 'regexp_replace' or 'replace' to remove potentially harmful characters or SQL keywords.
B. Use parameterized queries (also known as prepared statements) to separate SQL code from user-supplied data, treating user input as data rather than executable code.
C. Limit user input to a predefined set of options through dropdown menus or radio buttons to avoid arbitrary SQL code injection.
D. Enclose all dynamically generated SQL queries within a 'TRY...CATCH' block to catch and handle any potential SQL injection errors.
E. Rely solely on Snowflake's built-in security features and assume they automatically prevent all SQL injection attempts.

정답：A,B,C

설명：
Parameterized queries are the most effective way to prevent SQL injection. Sanitizing input also mitigates the risk. Limiting user input to a defined set is useful in general for usability and for SQL injection. Snowflake's built-in features do not prevent against this. 'TRY...CATCH' doesn't prevent injection, but rather catches it after the attempt.

질문 # 188
You are developing a Snowflake Native Application that allows customers to enrich their dat a. The application involves executing Javascript UDFs. Which of the following practices are considered unacceptable regarding code practices within the UDFs to ensure the Native App complies with Snowflake's requirements and security best practices?

A. Directly embedding secrets (e.g., API keys, passwords) within the Javascript UDF code itself, even if obfuscated.
B. Using the SYSTEM$ALLOWLIST function to restrict external network access from the Javascript UDFs to a specific set of approved domains. Using Snowflake's network rules is also acceptable for newer accounts.
C. Relying solely on the consumer's network policy to manage external network access from the UDFs.
D. Using Javascript UDFs to access data not needed by the application to save on querying costs.
E. Using JavaScript's eval() function to dynamically execute code based on user input or data retrieved from external sources.

정답：A,C,D,E

설명：
Options B, C, D and E are all unacceptable. Embedding secrets (B) directly in code is a major security risk. Using 'eval()' (C) opens the door to code injection vulnerabilities. Relying solely on the consumer's network policy (D) is insufficient; the application provider must also implement security measures. Javascript UDFs should only access required data by the application as this is also one of best practices regarding code practices within the UDFs. Option A is acceptable as it implements whitelisting of external network access.

질문 # 189
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