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Snowflake NAS-C01 Exam Syllabus Topics:

Snowflake SnowPro Specialty - Native Apps Sample Questions:

1. A data engineer needs to upload a large (10 GB) data file to an internal stage within a Snowflake Native App package using Snowsight. Which of the following methods is the MOST efficient and recommended approach, considering factors like network stability and potential interruptions?

A) Using a third-party file transfer tool (e.g., rsync) to upload the file to an external stage and then copying it to the internal stage using Snowflake SQL commands.
B) Using the Snowsight web interface directly to upload the file in a single operation.
C) Using the Snowflake CLI (SnowSQL) with the 'PUT command, enabling automatic retries and parallel uploads.
D) Compress the file before uploading using Snowsight, this reduces size and ensures faster upload.
E) Breaking the file into smaller chunks (e.g., 100 MB each) and uploading them sequentially using Snowsight.

2. A Native Application developer, Alice, is building a data processing pipeline within her Snowflake Native App that includes a scheduled task. The task executes a stored procedure which needs to access data from a table owned by the consumer account. The application package version '1.0.0' has been released to a consumer. The task has been created and suspended. Which of the following SQL commands are required for Alice, as the application provider, to grant the necessary privileges on the consumer's environment to enable the task to execute successfully when it is resumed? Select ALL that apply.

A) GRANT EXECUTE TASK ON TASK my_task TO APPLICATION ROLE app_public;
B) GRANT EXECUTE TASK ON ACCOUNT TO SHARE;
C) GRANT EXECUTE TASK ON TASK my_task TO APPLICATION ROLE app_owner;
D) GRANT EXECUTE MANAGED TASK ON ACCOUNT TO SHARE;
E) GRANT EXECUTE TASK ON DATABASE TO APPLICATION ROLE app_public;

3. You are developing a Snowflake Native App that processes streaming data from a Kafka topic. The app needs to continuously ingest data, perform real-time transformations, and store the results in a Snowflake table within the app's managed database. Which Snowflake feature(s) and approach(es) are BEST suited for implementing this data ingestion and processing pipeline within the Native App?

A) Leverage Snowflake Snowpipe Streaming to ingest data directly from the Kafka topic into the target table, combined with Snowflake Tasks to handle any required data transformations.
B) Implement a custom Kafka consumer within a Java User-Defined Function (UDF) to read data from the Kafka topic and directly insert it into the target table.
C) Use Snowflake Pipes to continuously ingest data from the Kafka topic into a staging table and then use stored procedures to periodically transform and load the data into the target table.
D) Use a Snowflake Stream to capture changes in the Kafka topic and then use a Snowflake Task to periodically merge these changes into the target table.
E) Employ a combination of Snowflake Snowpipe Streaming for data ingestion and Dynamic Tables for continuous, incremental data transformations within the application's managed database.

4. 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 E
B) Option D
C) Option B
D) Option C
E) Option A

5. You are troubleshooting an issue where a Snowflake Native Application's Scala UDF, 'process_data' , is sporadically failing with an 'OutOfMemoryError'. The UDF processes large XML files. You need to identify the cause and implement a solution. Which strategies are most appropriate for diagnosing and resolving this issue within the constraints of a Snowflake Native Application?

A) Implement caching mechanisms within the UDF to reuse previously processed XML data, reducing the need to load and parse the same data repeatedly.
B) Use a memory profiler to analyze the UDF's memory usage. Identify memory leaks or inefficient data structures that contribute to the 'OutOfMemoryErroN.
C) Modify the 'process_data' UDF to stream the XML file instead of loading the entire file into memory at once. Use a streaming XML parser to process the data incrementally.
D) Break down the large XML files into smaller chunks before processing them with the UDF. This reduces the memory footprint of each individual UDF execution. Implement a system to orchestrate processing chunks.
E) Increase the warehouse size for the consumer's account. This provides more memory to the UDF execution environment.

Solutions: