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Snowflake NAS-C01 Exam Syllabus Topics:

Snowflake SnowPro Specialty - Native Apps Sample Questions:

1. You're developing a Snowflake Native Application that performs complex data transformations within the consumer's account. These transformations require substantial compute resources. You want to optimize resource utilization and minimize cost for your consumers.
Which strategies can you employ to achieve this goal? (Select TWO)

A) Do not use transactions for data transformation pipelines as transactions introduce overhead.
B) Utilize Snowflake's auto-suspend and auto-resume features for virtual warehouses to minimize compute costs when the application is idle.
C) Implement dynamic scaling by programmatically adjusting the virtual warehouse size based on the workload using stored procedures and the function to cancel long running and inefficient tasks.
D) Implement data skipping techniques to minimize the amount of data scanned during transformations. This involves clustering tables appropriately.
E) Force consumers to use the largest possible virtual warehouse size to ensure transformations complete quickly.

2. You are developing a Snowflake Native Application that includes a Python UDE This UDF needs to interact with an external API that requires authentication using OAuth 2.0. The application package should securely manage the OAuth credentials without exposing them to the consumer account. How can you securely configure and access the OAuth credentials from within the UDF?

A) Use Snowflake's secret management feature to store the OAuth client ID and secret, and access them from the UDF using 'SYSTEM$GET SECRET.
B) Store the OAuth client ID and secret in environment variables within the Snowflake environment for the application.
C) Configure an OAuth integration in Snowflake and grant the UDF's role access to it, allowing the UDF to obtain tokens using 'SYSTEM$GET OAUTH TOKEN'.
D) Store the OAuth credentials in a secure external storage (e.g., AWS Secrets Manager) and retrieve them using an external function.
E) Store the OAuth client ID and secret directly in the UDF code as string literals.

3. A Snowflake Native App developer wants to externalize certain configuration values so that a consumer can adjust the app's behavior without modifying the app's code directly. Which of the following methods is the MOST appropriate for achieving this using the Snowflake Native App Framework?

A) Using Snowflake variables and setting them at the session level.
B) Hardcoding the configuration values directly into the SQL code.
C) Using dynamic SQL to modify the application code at runtime based on environment variables.
D) Defining parameters in the manifest file and using them within the application code.
E) Storing the configuration values in a separate Snowflake table within the consumer account.

4. You are developing a Snowflake Native Application that needs to access data residing in the consumer's account. The application requires specific permissions to read certain tables and execute specific functions. How should you define and manage these access privileges to ensure both security and functionality?

A) Define a custom role within the application package with the minimum required privileges (e.g., 'SELECT on specific tables, 'USAGE on specific functions). The consumer then grants this role to the application during installation. Use secure views if needed.
B) Rely on the consumer to manually grant the necessary privileges to the application's service account after installation. Provide clear instructions in the application's documentation.
C) Grant the 'ACCOUNTADMIN' role to the application. This provides unrestricted access to all resources in the consumer's account, simplifying development and deployment.
D) Create a new database role and grant the required permissions to the database role, later grant the database role to the application role. Use secure views for access to consumer data.
E) Use the 'GRANT OWNERSHIP' command to transfer ownership of the required tables and functions to the application's service account. This gives the application full control over these resources.

5. A Snowflake Native Application provider has encountered performance issues with a Scala-based UDF, 'aggregate data' , used within their application. Consumers report slow execution times when processing large datasets. The provider has identified that the UDF involves complex calculations and data transformations. Which optimization techniques can be applied to improve the performance of the 'aggregate_data' UDF within the constraints of the Snowflake Native Application environment?

A) Replace the Scala UDF with a Snowflake stored procedure written in SQL. Stored procedures generally offer better performance for data-intensive operations.
B) Utilize Snowflake's vectorized UDF feature (if supported for Scala UDFs) to process data in batches, reducing the overhead of calling the UDF for each individual row.
C) Optimize the UDF's data structures and algorithms to reduce memory usage and improve processing efficiency. Consider using techniques such as caching frequently accessed data within the UDF.
D) Leverage Scala's parallel collections (e.g., 'par') to distribute the workload across multiple cores during data processing. Ensure that the UDF is designed to be thread-safe.
E) Move the entire UDF logic to an external cloud function (e.g., AWS Lambda, Azure Function) and invoke it from Snowflake using a remote service. This offloads the computation to a more powerful environment.

Solutions: