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Snowflake SnowPro Specialty - Native Apps Sample Questions:

1. A Snowflake Native App is designed to transform data in the consumer's account. The application requires temporary write access to a staging table owned by the consumer. Which of the following SQL statements, when executed within the application package's logic, correctly grants the application role the necessary privileges to write to the consumer-owned staging table named 'CONSUMER STAGING TABLE in schema 'CONSUMER SCHEMA'?

A) GRANT INSERT, UPDATE, DELETE ON TABLE CONSUMER_SCHEMA.CONSUMER STAGING_TABLE TO APPLICATION ROLE app_role WITH GRANT OPTION;
B) GRANT ALL ON TABLE CONSUMER SCHEMCONSUMER_STAGING TABLE TO APPLICATION ROLE app_role;
C) GRANT SELECT, INSERT, UPDATE, DELETE, TRUNCATE ON TABLE CONSUMER SCHEMA.CONSUMER STAGING TABLE TO APPLICATION ROLE app_role;
D) GRANT INSERT, UPDATE, DELETE ON TABLE TO APPLICATION ROLE app_role;
E) GRANT INSERT, UPDATE, DELETE, TRUNCATE ON TABLE CONSUMER SCHEMA.CONSUMER STAGING TABLE TO APPLICATION ROLE app_role WITH GRANT OPTION;

2. You are the provider of a Snowflake Native Application built with Scal
a. Consumers are reporting that a specific UDF, 'calculate_risk' , occasionally returns incorrect results for certain input datasets. You suspect an issue with the UDF's logic but struggle to reproduce the problem in your development environment. Given that you cannot directly access the consumer's data, what are the most effective strategies for diagnosing this issue?

A) Modify the UDF to include a 'debug' mode that performs additional checks and assertions. The consumer can enable this mode to collect diagnostic information and report any failures back to the provider. Consumer shares the error logs/code
B) Implement comprehensive unit tests for the 'calculate_risk' UDF, covering a wide range of input values and edge cases. Ensure these tests pass before deploying new versions of the application.
C) Request the consumer to execute the UDF with the problematic datasets and provide the resulting incorrect output. Then, use Snowflake's query history and lineage features to trace the data flow and identify potential issues in the UDF's logic.
D) Implement logging within the 'calculate_risk' UDF to capture input parameters and output values for a sample of executions. Store these logs in a secure table accessible only to the provider, subject to data governance policies. Utilize Snowflake's masking policies to mask the sensitive data and allow the provider to see only masked version, not the actual sensitive data.
E) Request the consumer to provide anonymized sample data that triggers the issue. Use this data in your development environment to debug the UDF.

3. You are designing a Snowflake Native Application package. You need to include a specific version of a Javascript UDF that calculates a complex statistical metric. You want to ensure that even if you update the application in the future, older installations continue to use the original Javascript UDF version. You also want to ensure that the javascript code is obfuscated. How do you design the application package directory structure and the UDF creation to achieve this? (Assume the UDF javascript source is named 'metric_vl .js' and is placed in the correct location within the application source code directory). The relevant 'manifest.yml' excerpt is:

A) Option B
B) Option E
C) Option C
D) Option D
E) Option A

4. You are creating a Snowflake Native Application that will be distributed to consumer accounts. As part of the installation process, you need to ensure that a specific database role, 'DATA READER, is created in the consumer account and granted 'SELECT' privileges on a specific table in the consumer's shared dat a. Which of the following code snippets, when placed within the application's setup script (install.sql), will correctly achieve this, assuming the application is granted 'CREATE DATABASE ROLE?

A)

B)

C)

D)

E)

5. 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) Use a Snowflake Stream on the event table and continuously poll the Stream from within the Snowpark Container Service using the Snowpark API.
B) 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.
C) 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.
D) 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.
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.

Solutions: