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Snowflake NAS-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Native Application Design and Creation	35%	- Application development workflow 1. Application roles and privileges 2. Building and versioning applications
Snowflake Native App Framework Overview	20%	- Native application concepts and architecture 1. Application packages and structure 2. Manifest files and setup scripts
Deployment and Marketplace Publishing	25%	- Distribution and lifecycle management 1. Version upgrades and release management 2. Marketplace listings and publishing
Installation, Testing and Troubleshooting	20%	- Validation and debugging 1. Logging and event tables 2. Tracing and troubleshooting workflows

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Snowflake SnowPro Specialty - Native Apps Sample Questions (Q119-Q124):

NEW QUESTION # 119
Your Snowflake Native Application needs to integrate with an external Python library (e.g., 'requests' ) to fetch data from a third-party API. You have packaged the library using Anacond a. How do you correctly reference and use this external library within a Python UDF in your application?

A. Install the 'requests' library on each Snowflake virtual warehouse used by the application.
B. Use 'snowflake.ingest.SimplelngestManager' to manage the library dependencies within the application's metadata.
C. Specify the library in the 'imports' clause when creating the UDF using the opackageS decorator. For example:
D. Import the library directly in the UDF code using 'import requests'. Snowflake automatically handles external dependencies.
E. Create a stage to store the Anaconda packages and reference them when calling the UDF from Streamlit.

Answer: C

Explanation:
Snowflake uses Anaconda for managing Python dependencies in UDFs. The correct way to specify external libraries is through the packages parameter in the '@sproc' decorator or when creating a UDF using 'CREATE FUNCTION'. Directly importing without specifying the package or managing it manually (C, D, E) will lead to A is wrong since the library has to be explicitly declared as a dependency.

NEW QUESTION # 120
You are creating a Snowflake Native Application that uses Snowpark Container Services. The application needs to process large datasets and requires a Compute Pool with specific GPU resources. You use the following SQL command to create a compute pool:

After creating the compute pool, you notice that your Snowpark Container Service is unable to start. Upon inspecting the 'SYSTEM$GET CP POLICY' for the allowed instance families list does not contain 'GPU NV M'. What is the MOST likely reason for this and how do you rectify it?

A. The account does not have access to GPU-based instance families. Contact Snowflake support to enable GPU instance families for your account.
B. GPU instance types are automatically enabled as part of SPCS and there is no account restriction.
C. You need to explicitly grant the 'USAGE privilege on the instance family to the application role used by the container service using "GRANT USAGE ON INSTANCE FAMILY GPU NV M TO ROLE
D. The specified instance family is invalid or not supported in your region. Use 'SYSTEM$GET_CP POLICY to verify the available instance families and choose a supported one.
E. The COMPUTE POOL needs to be explicitly associated to the application package by running ALTER APPLICATION PACKAGE ADD COMPUTE POOL my_compute_poor.

Answer: A

Explanation:
GPU instance families are not automatically enabled for all accounts. Access must be explicitly granted. Without the necessary account permissions, the compute pool creation might succeed, but the container service will fail to start. The account admin needs to contact Snowflake to enable it.

NEW QUESTION # 121
An ISV is developing a data analytics application using Snowflake Native Apps. The application allows consumers to define custom data refresh schedules. These schedules are managed by stored procedures within the application. The stored procedures need to update a shared table (using SQL MERGE command) located in a different database (managed by the ISV) within the consumer's account. The Application provider doesn't want to give access to the underlying tables to the consumer. To achieve this, which of the following GRANT statements are needed to configure the correct set of privileges for the ISV's Application Role(s) to perform MERGE operations from inside the consumer's environment? (Select all that apply.)

A. GRANT SELECT ON ALL TABLES IN DATABASE TO ROLE ;
B. GRANT USAGE ON SCHEMA TO ROLE ;
C. GRANT ALL PRIVILEGES ON WAREHOUSE TO ROLE ;
D. GRANT USAGE ON DATABASE TO ROLE ;
E. GRANT INSERT, UPDATE, DELETE ON TABLE .. TO ROLE ;

Answer: B,D,E

Explanation:
To perform MERGE operations, the ISV application needs the following privileges: USAGE on the ISV database. USAGE on the ISV schema. INSERT, UPDATE, and DELETE on the shared table. SELECT is not enough for MERGE. Granting ALL PRIVILEGES on the warehouse is too broad and unnecessary for the specific task. Granting select on all tables is not required either as you're targeting a single table for DML operation.

NEW QUESTION # 122
A developer is building a Snowflake Native App that requires secure storage of sensitive application configuration dat a. Which of the following Snowflake features is the MOST appropriate to use within the application package to manage and secure this data?

A. Encrypting the data with a customer-managed key (CMK) stored outside of Snowflake and decrypting it within a UDF.
B. Snowflake Secrets, accessed via SQL commands and managed through object privileges.
C. Using a Stage to store encrypted configuration files, accessible by the application using external functions.
D. Snowflake Stored Procedures with 'CALLER rights, encrypting the data within the procedure.
E. Storing the data in a standard Snowflake table within the application package, relying on Snowflake's built-in encryption.

Answer: B

Explanation:
Snowflake Secrets are specifically designed for storing sensitive information like API keys and passwords within Snowflake. They provide a secure way to manage and access this data without exposing it directly in code. They leverage Snowflake's object privileges to control access.

NEW QUESTION # 123
You are developing a Snowflake Native App using Python that performs complex data transformations using UDFs and external functions. During consumer testing, you observe intermittent 'Remote service timed out' errors when calling the external function. The external function is hosted on AWS Lambd a. Which combination of the following actions would BEST address this issue, ensuring reliability without compromising security?

A. Increase the timeout setting for the external function in Snowflake, optimize the Lambda function code for faster execution, and monitor Lambda function metrics (e.g., duration, errors) using CloudWatch. Also, increase the memory allocation for the AWS Lambda function.
B. Implement retry logic in the Python code calling the external function with exponential backoff, and configure Snowflake to use a larger warehouse size to improve network bandwidth. Furthermore, check if AWS Lambda throttles the execution of requests.
C. Migrate the external function to a Snowflake Java UDF to minimize network latency, and reduce the payload size sent to the external function. Verify that the AWS Lambda function and Snowflake instance are in the same region.
D. Cache the results of the external function calls in a Snowflake table to reduce the number of calls to the external service, and implement robust error handling and logging in both the Snowflake UDF and the Lambda function. Always return a value, even in case of error.
E. Implement queue-based processing for external function calls using Snowflake streams and tasks, leverage a message queue service (e.g., SQS) to buffer requests, and ensure the Lambda function has sufficient concurrency and is not being throttled.

Answer: A

Explanation:
Increasing the timeout in Snowflake, optimizing the Lambda function's performance (including code optimization and increasing memory allocation), and monitoring Lambda metrics are critical for addressing timeout issues. Lambda timeouts can occur if the function takes too long to execute, so increasing the timeout and optimizing performance can directly mitigate this. Furthermore, checking Lambda function metrics in CloudWatch can provide valuable insights into the root cause of performance problems. This combination tackles both the symptom (timeouts) and potential underlying causes (slow execution, resource constraints). Other answers only deal with some of the issues; without a performance-focused perspective, the problem could persist.

NEW QUESTION # 124
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