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Snowflake NAS-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Snowflake Native Applications Design and Creation	35%	- Create and manage billing events and cost monitoring techniques 1. Formulate Snowflake native application workflows and procedures - Apply Snowflake best practices while building native application workloads 1. Build, distribute, and monetize apps in Snowflake 2. Design scalable applications
Snowflake Native App Framework Overview	20%	- Understand Snowflake architecture, features, tools, and best practices 1. Cloud-based computing and storage concepts 2. Account security and access management 3. Data sharing and protection mechanisms
Snowflake Native Applications Deployment	25%	- Build, version, and release native applications 1. Use data sharing for app interoperability 2. Use Snowflake's marketplace
Snowflake Native Applications Installation and Testing	20%	- Install and test native applications 1. Validate application functionality and performance

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Snowflake SnowPro Specialty - Native Apps Sample Questions (Q185-Q190):

NEW QUESTION # 185
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. 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
C. GPU instance types are automatically enabled as part of SPCS and there is no account restriction.
D. The COMPUTE POOL needs to be explicitly associated to the application package by running ALTER APPLICATION PACKAGE ADD COMPUTE POOL my_compute_poor.
E. 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.

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 # 186
You've deployed a Snowflake Native Application to the Marketplace. A consumer reports an issue, and after investigating, you suspect a vulnerability related to SQL injection within a stored procedure. You want to implement best practices to mitigate this risk. Which of the following strategies are recommended to secure the stored procedure against SQL injection?

A. Implement strict input validation and sanitization to ensure that user input conforms to expected data types and patterns.
B. Encode all user input before inserting it into SQL queries to neutralize potentially harmful characters.
C. Disable all external network access from within the stored procedure environment.
D. Use parameterized queries or prepared statements whenever possible to prevent user input from being directly interpreted as SQL code.
E. Grant 'OWNERSHIP' privilege to the stored procedure to prevent unauthorized modifications.

Answer: A,B,D

Explanation:
SQL injection vulnerabilities arise when user input is incorrectly incorporated into SQL queries. Parameterized queries (A) and input validation/sanitization (B) are crucial defenses. Parameterized queries treat input as data, not code, preventing injection. Input validation ensures only expected data reaches the query. Encoding user input (D) neutralizes harmful characters that could be exploited. Granting 'OWNERSHIP' (C) is for access control, not injection prevention. Disabling network access (E) might improve overall security but doesn't directly address SQL injection within the procedure.

NEW QUESTION # 187
A software vendor is developing a Snowflake Native App that provides data enrichment services. As part of the application lifecycle, they need to automate the process of packaging, deploying, and upgrading their application across multiple Snowflake regions. Which of the following approaches provide the MOST efficient and reliable way to automate the deployment and upgrade process for this Native App? Select TWO options.

A. Integrate with CI/CD pipelines, creating packages and updating versions to control the application's deployment and upgrade process.
B. Use Snowflake's CLI (SnowSQL) in conjunction with scripting tools (e.g., Bash, Python) to automate the creation of application packages and deployments.
C. Create a custom deployment tool that directly manipulates Snowflake's metadata tables to register the application package.
D. Manually create application packages for each region and upload them to Snowflake using the web interface.
E. Leverage Snowflake's Native Apps API (if available) to programmatically manage the application package lifecycle.

Answer: A,B

Explanation:
Using Snowflake's CLI (SnowSQL) with scripting allows for automating tasks like creating application packages and deployments. Integrating with CI/CD pipelines automates the entire deployment process, from building the application package to deploying it to multiple regions. Manual package creation is inefficient (A). Directly manipulating Snowflake's metadata tables is not recommended and unsupported (D). While Snowflake's Native Apps API (C) would be ideal, at the time of this writing, a fully featured API may not be available, so CLI scripting is currently the better option. CI/CD integration is also a valid response for this.

NEW QUESTION # 188
A Snowflake Native Application, leveraging Snowpark Container Services (SPCS), is designed to perform complex data transformations on data residing within the customer's Snowflake account. The transformations require significant computational resources, and the container occasionally encounters resource constraints, leading to performance degradation. Which of the following strategies can you employ to optimize resource utilization and ensure the container runs efficiently within the customer's SPCS environment?

A. Rewrite the data transformation logic to utilize Snowflake's built-in SQL functions and stored procedures whenever possible. Move the transformation workload out of the container.
B. Optimize the container image by removing unnecessary dependencies and libraries, reducing its overall size and startup time.
C. Increase the size of the virtual warehouse associated with the SPCS compute pool. This directly provides more computational resources to the container.
D. Implement horizontal scaling within the container by deploying multiple replicas of the application and distributing the workload among them using a load balancer. The consumer must configure the number of replicas.
E. Increase the memory and CPU limits specified in the 'spec.yaml' file for the Snowpark Container Services deployment. This provides the container with more resources within the allocated compute pool.

Answer: A,B,E

Explanation:
Options B, D, and E are all effective strategies. Option B reduces the container's resource footprint. Option D offloads the resource- intensive transformations to Snowflake's native engine. Option E increases the resource allocation for the container, and setting limits ensures containers cannot monopolize resources. Option A does not directly control container resources. Option C is valid but, the provider should consider how the consumer would set replicas, as typically, the provider should have no control over customer resources.

NEW QUESTION # 189
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. 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.
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. 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.
D. 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.
E. 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.

Answer: D

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 # 190
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