Files arrive in an external stage every 10 seconds from a proprietary system. The files range in size from 500 K to 3 MB. The data must be accessible by dashboards as soon as it arrives.
How can a Snowflake Architect meet this requirement with the LEAST amount of coding? (Choose two.)

• A.Use Snowpipe with auto-ingest.
• B.Use a COPY command with a task.
• C.Use a materialized view on an external table.
• D.Use the COPY INTO command.
• E.Use a combination of a task and a stream.

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A Snowflake Architect is designing a multiple-account design strategy.
This strategy will be MOST cost-effective with which scenarios? (Select TWO).

• A.The company wants to clone a production database that resides on AWS to a development database that resides on Azure.
• B.The company needs to share data between two databases, where one must support Payment Card Industry Data Security Standard (PCI DSS) compliance but the other one does not.
• C.The company needs to support different role-based access control features for the development, test, and production environments.
• D.The company security policy mandates the use of different Active Directory instances for the development, test, and production environments.
• E.The company must use a specific network policy for certain users to allow and block given IP addresses.

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What are some of the characteristics of result set caches? (Choose three.)

• A.Each time persisted results for a query are used, a 24-hour retention period is reset.
• B.The data stored in the result cache will contribute to storage costs.
• C.Time Travel queries can be executed against the result set cache.
• D.The retention period can be reset for a maximum of 31 days.
• E.Snowflake persists the data results for 24 hours.
• F.The result set cache is not shared between warehouses.

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Bytes spilled to remote storage in query profile indicates volume of data spilled to remote disk

• A.FALSE
• B.TRUE

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A media company needs a data pipeline that will ingest customer review data into a Snowflake table, and apply some transformations. The company also needs to use Amazon Comprehend to do sentiment analysis and make the de-identified final data set available publicly for advertising companies who use different cloud providers in different regions.
The data pipeline needs to run continuously and efficiently as new records arrive in the object storage leveraging event notifications. Also, the operational complexity, maintenance of the infrastructure, including platform upgrades and security, and the development effort should be minimal.
Which design will meet these requirements?

• A.Ingest the data using Snowpipe and use streams and tasks to orchestrate transformations. Export the data into Amazon S3 to do model inference with Amazon Comprehend and ingest the data back into a Snowflake table. Then create a listing in the Snowflake Marketplace to make the data available to other companies.
• B.Ingest the data using copy into and use streams and tasks to orchestrate transformations. Export the data into Amazon S3 to do model inference with Amazon Comprehend and ingest the data back into a Snowflake table. Then create a listing in the Snowflake Marketplace to make the data available to other companies.
• C.Ingest the data into Snowflake using Amazon EMR and PySpark using the Snowflake Spark connector. Apply transformations using another Spark job. Develop a python program to do model inference by leveraging the Amazon Comprehend text analysis API. Then write the results to a Snowflake table and create a listing in the Snowflake Marketplace to make the data available to other companies.
• D.Ingest the data using Snowpipe and use streams and tasks to orchestrate transformations. Create an external function to do model inference with Amazon Comprehend and write the final records to a Snowflake table. Then create a listing in the Snowflake Marketplace to make the data available to other companies.

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