最新のNAS-C01問題集で試験を準備する

Snowflake NAS-C01 試験概要:

Snowflake NAS-C01 試験シラバストピック:

Snowflake SnowPro Specialty - Native Apps 認定 NAS-C01 試験問題：

1. A data science company is developing a Snowflake Native App that provides advanced fraud detection for financial institutions. The app requires secure access to sensitive transaction data within the consumer's Snowflake account. Which of the following approaches BEST aligns with Snowflake's recommended practices for managing data access and securing data within a Native App?

A) The app should request full ACCOUNTADMIN privileges in the consumer account to access all necessary data for fraud detection.
B) The app should directly access transaction data through data sharing from provider account.
C) The app should use secure views created by the consumer that grant the app read-only access to only the specific transaction data needed for fraud detection.
D) The app should create and manage its own temporary tables in the consumer account and load transaction data into them for processing.
E) The app should directly access tables containing transaction data using the consumer's own service account credentials, which are securely stored within the app's code.

2. You are developing a Snowflake Native App that allows consumers to enhance their existing customer data with enrichment data provided by your app. The app relies on secure data sharing. To minimize data duplication and maximize query performance within the consumer's account, which of the following approaches offers the MOST optimized and secure way to deliver the enrichment data?

A) Create a secure view within the application package itself, which joins the application's enrichment data with consumer-provided data via an API integration. The consumer queries this secure view.
B) Create a staging table in the provider account, load the enrichment data into it, and grant the consumer account SELECT privileges on this staging table. The consumer then creates their own table and copies the data.
C) Use Snowflake Data Marketplace listing to provide enrichment data.
D) Utilize Snowflake Secure Data Sharing to directly share the enrichment data tables from the provider account to the consumer account. The consumer can then create views on the shared tables or query them directly.
E) Develop a Snowflake UDF (User-Defined Function) that, when called in the consumer account, retrieves the enrichment data from an external API endpoint (maintained in the provider account) and returns it.

3. You are developing a Snowflake Native Application that processes customer orders. The application needs to securely store sensitive customer data (e.g., credit card numbers) within the provider's account but must grant the consumer's account access to analyze aggregated, de-identified order statistics. Which combination of features is BEST suited to achieve this while adhering to security best practices and Snowflake Native App constraints?

A) Store sensitive data in an internal stage within the provider account. Create a stored procedure that reads data from the stage, aggregates and de-identifies it, and writes the results to a shareable table. The consumer application can query the shareable table.
B) Store sensitive data in a secured view within the provider account. Grant the consumer account usage privileges on this secured view. Create a setup script using 'CREATE APPLICATION ROLE and 'GRANT to give the consumer's application role access to the secured view.
C) Store sensitive data in a table within the provider account. Create a user-defined function (UDF) that aggregates and de-identifies the data. Share the table and the UDF with the consumer account through a share. The consumer application can then use the UDF to query the data.
D) Store sensitive data in a table within the provider account. Use dynamic data masking policies to de-identify the data when accessed by the consumer account. Share the underlying table directly with the consumer account through a share.
E) Store sensitive data in a table within the provider account. Create a stored procedure that aggregates and de-identifies the data, writing the results to a new table. Share this new, aggregated table with the consumer account via a data share.

4. You are preparing to deploy a Snowflake Native Application to the Snowflake Marketplace. You have implemented versioning using the 'VERSION' clause in your setup script. Your application relies on a specific shared data source that is only available in the AWS us- west-2 region. To ensure that consumers in other regions can still install and use your application, you decide to offer different versions with region-specific logic. Which of the following approaches is the MOST effective and recommended to manage region-specific configurations and data sharing across multiple versions of your Snowflake Native Application?

A) Implement conditional logic within the application's stored procedures to check the current region and adjust data sharing and execution accordingly using Package all configurations within a single version.
B) Create separate application packages for each region, containing region-specific configurations and data sharing setups. This ensures consumers only install the version relevant to their region.
C) Use separate schemas within the application package for each region, and configure application roles with permissions to only access schemas relevant to their region. No changes to code logic are required.
D) Implement a build process that generates region-specific setup scripts based on environment variables using a CI/CD pipeline. Each version of the application will then contain the right setup script based on the target region.
E) Use the 'PATCH' clause during application deployment to selectively modify the configuration based on the target region. Deploy the same application package to all regions.

5. You are designing a Snowflake Native Application that involves data sharing with consumers. You want to leverage a custom Python library 'data_transform.py' within your application package to perform specific data transformations on the shared data before it is accessed by consumers. This library contains sensitive logic and should not be directly visible or accessible by the consumer. Which of the following is the MOST secure and appropriate way to package and utilize this 'data_transform.py' library?

A) Embed the contents of 'data_transform.py' directly within the application's stored procedure code as a multi-line string.
B) Include directly within your application package's source code and call its functions from within your application's procedures. Grant necessary privileges on the procedures to the consumer's role, ensuring to only expose the procedures and never the python source.
C) Package 'data_transform.py' as a user-defined function (UDF) and grant 'USAGE privilege on the UDF to the consumer's role.
D) Store 'data_transform.py' as an internal stage file and read it dynamically into your application's Python code at runtime.
E) Publish 'data_transform.py' as a public Python package on Anaconda and have consumers install it in their Snowflake environment.

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