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Snowflake SnowPro Specialty - Native Apps Sample Questions:

1. You are developing a Snowflake Native Application that relies on external Python packages. You've successfully added the necessary packages to the stage associated with your application package using 'snowflake-cli' during development. However, when testing the application in a consumer account, you encounter errors indicating that these packages are not found. What are the most likely reasons for this issue and how can you resolve them?

A) The 'snowflake.yml' file does not correctly specify the stage where the Python packages are located. Update the 'snowflake.yml' file to include the correct stage URL and ensure the application's setup script creates the stage and uploads the packages.
B) The consumer account's Python environment is incompatible with the packages used by the application. Instruct the consumer to update their Python environment to match the application's requirements.
C) The packages were not correctly added to the stage. Verify that the packages are in the stage specified in the 'snowflake.yml' file, and that the application has the necessary permissions to read from the stage within the setup script.
D) The consumer account does not have access to the Snowflake Marketplace where the packages are hosted. Request the consumer to enable access to the required marketplace listings.
E) The stage is not automatically shared with the consumer account as part of the application package. Ensure that your setup script includes a step to create a share and grant usage on the stage to the consumer account.

2. You are publishing a Snowflake Native Application to the Marketplace. You've completed initial testing, and the application appears to function as expected. However, the security scan consistently fails with a 'Data Exfiltration' error. The application includes a stored procedure that aggregates data from several tables within the application container and writes the results to an external stage using the 'COPY INTO' command. Review the following code snippet:

Which of the following changes is MOST likely to resolve the 'Data Exfiltration' error during the security scan?

A) Modify the stored procedure to only export data to a table within the application container instead of an external stage.
B) Require the consumer to explicitly grant the application usage privilege on a secure external stage controlled by the consumer, and modify the procedure to validate the stage's existence and permissions before exporting data.
C) Remove the 'GRANT EXECUTE privilege on the 'export_data' procedure from the 'app_public' role.
D) Encrypt the data written to the external stage using client-side encryption with a key managed by the application provider.
E) Implement row-level security policies on the 'aggregated_data' table to restrict access to sensitive rows.

3. 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) Encode all user input before inserting it into SQL queries to neutralize potentially harmful characters.
B) Disable all external network access from within the stored procedure environment.
C) Implement strict input validation and sanitization to ensure that user input conforms to expected data types and patterns.
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.

4. You are developing a Snowflake Native Application using Scala for a financial institution. The application processes sensitive transaction dat a. During consumer-side testing, the consumer reports intermittent 'java.lang.NullPointerException' errors. You suspect the issue lies within your UDE How can you effectively trace and debug this issue, considering data privacy requirements and without directly exposing sensitive consumer data in your logs?

A) Use Snowflake's event table to capture all exceptions, filtering for the UDF name. Redact potentially sensitive fields in the logs after extraction using a Python script executed on the provider side.
B) Rely solely on Snowflake's query history to identify failing queries. Analyze the input data and execution plan to infer the cause of the NullPointerException.
C) Implement structured logging within the Scala UDF using SLF4J and Logback. Include contextual information (e.g., transaction IDs, timestamps) at various stages of the UDF execution. Use Snowflake's masking policies on the event table to protect sensitive data during logging. This is the best method as it's the more granular and effective
D) Implement a custom error handling mechanism in Scala that catches 'NullPointerException', logs a generic error message with a unique error code to the event table, and then re-throws a more descriptive exception to the consumer. The consumer can then report the error code to the provider for further investigation. The provider can analyze the code, without actual consumer sensitive data.
E) Utilize 'System.out.println' statements within the Scala UDF to print debug messages directly to the query result. The consumer can view these messages to identify the source of the error, with the provider having no visibility.

5. 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) 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.
B) 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
C) Request the consumer to provide anonymized sample data that triggers the issue. Use this data in your development environment to debug the UDF.
D) 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.
E) 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.

Solutions: