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Snowflake NAS-C01 Exam Syllabus Topics:

Snowflake SnowPro Specialty - Native Apps Sample Questions:

1. You are developing a Snowflake Native Application that utilizes Snowpark Container Services (SPCS). Your application requires a Compute Pool configured with autoscaling capabilities to handle fluctuating workloads. However, you want to minimize costs when the application is idle. Which of the following considerations are MOST important when defining the 'MIN NODES' and 'MAX NODES' parameters for your Compute Pool, along with the 'AUTO RESUME setting?

A) The 'AUTO_RESUME parameter is irrelevant as the compute pool automatically scales based on current workload. should be set to the expected average workload.
B) Setting to a non-zero value ensures that some resources are always available, leading to faster response times for initial requests. 'AUTO RESUME' should be set to FALSE to prevent unnecessary resource usage.
C) The optimal configuration depends on the balance between cost and performance. A higher 'MIN_NODES' value provides faster initial response times but incurs higher costs when idle. 'AUTO RESUME should be set to TRUE if you want the pool to automatically restart based on incoming requests.
D) Setting to 0 allows the Compute Pool to scale down completely when idle, eliminating costs. 'AUTO_RESUME should be set to TRUE to automatically start the pool when a new request is received.
E) Compute Pool autoscaling is managed entirely by the underlying container orchestration system and Snowflake only provides basic resource allocation. The 'MIN NODES' and 'MAX NODES parameters are advisory only.

2. You are developing a Snowflake Native App that needs to access a secured external API to enrich customer dat a. The API requires an OAuth 2.0 token stored securely. Which of the following approaches would you use within the application manifest and Snowflake objects to securely store and access this token, minimizing exposure and adhering to best practices?

A) Store the OAuth 2.0 token directly in the application's manifest file as a PARAMETER. This simplifies access within the application code.
B) Use Snowflake's SECRET object type to securely store the OAuth 2.0 token. Reference the SECRET object within the application using PARAMETERS in the manifest and utilize function in application code to retrieve it at runtime.
C) Store the OAuth 2.0 token as a cleartext secret in a Snowflake stage and access it using external functions.

D) Store the OAuth 2.0 token as a masked comment within the application's SQL code. This will provide sufficient obfuscation.
E) Hardcode the OAuth 2.0 token within the application's stored procedures. This ensures the token is readily available.

3. A company is developing a Snowflake Native Application that will be distributed through the Snowflake Marketplace. The application uses internal stages to store intermediate data processing results. Which of the following methods is the MOST secure and recommended way to manage the application's internal stages?

A) Create an internal stage owned by the application database role and grant usage to all roles within the application.
B) Create an internal stage owned by the ACCOUNTADMIN role and grant usage to the application database role.
C) Create an internal stage owned by a custom role created specifically for stage management within the application, granting only necessary permissions.
D) Create an external stage in AWS S3 or Azure Blob Storage and grant access to the application database role.
E) Create an internal stage owned by the application database role. The application framework automatically grants necessary permissions.

4. 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 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.
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 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.
D) 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.
E) 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.

5. A Native App developer is using Snowflake's Event Tables for tracing the execution of their application. They have noticed a significant performance overhead when event logging is enabled, particularly for high-volume UDFs. Which of the following strategies would be MOST effective in mitigating the performance impact of event logging while still capturing valuable tracing information?

A) Disable event logging entirely for high-volume UDFs to eliminate the performance overhead.
B) Increase the frequency of event table flushes to reduce the number of events buffered in memory. This minimizes memory pressure and write latency.
C) Reduce the number of columns captured in the event tables to minimize the amount of data being written. Store all tracing data in a single large event table to simplify querying.
D) Implement sampling or conditional logging within the UDFs, only logging events for a subset of executions or when specific conditions are met. Utilize asynchronous logging techniques where appropriate to offload logging operations.
E) Write event data directly to an external cloud storage system (e.g., AWS S3, Azure Blob Storage) bypassing Snowflake's Event Tables altogether.

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