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Snowflake SnowPro Specialty - Native Apps Sample Questions:
1. Consider the following Python code snippet used within a Streamlit application deployed as a Snowflake Native App:
A) The code will execute successfully, assuming that Smy_table' exists, and the 'app_public' role has at least SELECT privilege on 'my_table' and USAGE privilege on the schema containing 'my_table' .
B) The code requires using the 'snowflake.snowpark' library instead of for optimized performance within native apps.
C) The code will execute without any privilege errors, assuming the necessary database and schema privileges are granted to the 'app_public' role.
D) The code will fail because the Snowflake Connector for Python requires explicit connection parameters to be defined even within a Native App context.
E) The code will fail with a privilege error if the 'app_public' role does not have OWNERSHIP of the table 'my_table' .
2. You are designing a Snowflake Native Application using Snowpark Container Services to perform real-time sentiment analysis on streaming dat a. The application needs to subscribe to a Snowflake event table to receive incoming data, perform the analysis in the container, and store the results in another table in the consumer's account. Which of the following approaches represents the most efficient and scalable way to implement this data ingestion and processing pipeline?
A) Implement a Snowpipe pipeline that continuously loads data from an external stage into the event table. Then use a database trigger on the event table to invoke a stored procedure that calls the Snowpark Container Service.
B) Use a Snowflake Stream on the event table and continuously poll the Stream from within the Snowpark Container Service using the Snowpark API.
C) Utilize Snowflake Event Tables and subscribe the Snowpark Container Service to the event table. Process incoming events using a Snowpark Python function within the container, leveraging its ability to handle concurrent requests.
D) Configure a Snowflake Task to periodically load data from the event table into an internal stage and then trigger the Snowpark Container Service to process the data from the stage.
E) Create a Snowflake external table that directly reads data from the external source. Mount this external table within the container service and perform sentiment analysis directly on the external data.
3. You are developing a Snowflake Native Application that needs to track usage metrics within consumer accounts. Specifically, you want to log the number of times a particular function within your application is called, and also store certain operational statistics. Which of the following storage options are available to you within the application package to persist these metrics, and what are their implications?
(Select TWO)
A) Leveraging external stages (e.g., AWS S3) to store the metrics. The application would need the necessary IAM roles and stage configuration.
B) Using temporary tables within the application. These tables are automatically dropped at the end of the session.
C) Using the 'APPLICATION' schema to create persistent internal stages. Data written to these stages will persist across installations and updates.
D) Utilizing User-Defined Functions (UDFs) with external network access to push metrics to an external logging service.
E) Creating permanent tables within the application package using the 'APPLICATION' schema. These tables persist across installations and updates.
4. You are developing a Snowflake Native Application that processes sensitive financial data. You need to implement robust logging and auditing to comply with regulatory requirements. Which of the following strategies would provide the MOST secure and comprehensive auditing and logging solution within the context of a Snowflake Native App?
A) Use Snowflake's stages to write the event logs and then create the external table on top of it to process the data.
B) Implement logging to an external service such as AWS CloudWatch or Azure Monitor, streaming data directly from UDFs using external functions. Implement data redaction within the UDFs before sending data externally.
C) Rely solely on Snowflake's ACCOUNT_USAGE views and information schema tables within the consumer account.
D) Utilize Snowflake's Event Tables to capture application events. Ensure proper role-based access control is in place to restrict access to the event data within the provider account. Masking and Row Access policies should be applied before writing to event tables.
E) Implement custom logging using UDFs that write directly to a table accessible only within the provider account. Use masking policies to protect sensitive data before logging.
5. You are developing a Snowflake Native App that allows consumers to configure certain parameters through a setup page. You want to persist these configurations securely. What is the recommended approach to store and retrieve consumer-specific configuration data within a Snowflake Native App?
A) Create a dedicated configuration table within the consumer's Snowflake account using the setup script and use secure UDFs to access it.
B) Utilize the 'APPLICATION_SETTINGS' object to store and retrieve configuration parameters. Access these settings within your application logic.
C) Store the configuration data as environment variables within the application package.
D) Store the configuration data in a secured view provided by the provider and accessible to the consumer.
E) Store the configuration data in a consumer-specific stage created during app installation.
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: C | Question # 3 Answer: C,E | Question # 4 Answer: D | Question # 5 Answer: B |





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