ARA-C01 · Question #143
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…
The correct answer is B. Ingest the data using Snowpipe and use streams and tasks to orchestrate transformations. Create. Each requirement maps to a specific Snowflake feature in option B: (1) Snowpipe with event notifications handles continuous, event-driven ingestion from object storage with minimal infrastructure management; (2) streams and tasks orchestrate transformations efficiently as new…
Question
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 ang 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?
Options
- AIngest the data using COPY INTO and use streams and tasks to orchestrate transformations.
- BIngest the data using Snowpipe and use streams and tasks to orchestrate transformations. Create
- CIngest the data into Snowflake using Amazon EMR and PySpark using the Snowflake Spark
- DIngest the data using Snowpipe and use streams and tasks to orchestrate transformations. Export
How the community answered
(34 responses)- A26% (9)
- B56% (19)
- C12% (4)
- D6% (2)
Explanation
Each requirement maps to a specific Snowflake feature in option B: (1) Snowpipe with event notifications handles continuous, event-driven ingestion from object storage with minimal infrastructure management; (2) streams and tasks orchestrate transformations efficiently as new records arrive; (3) an external function calling Amazon Comprehend performs sentiment analysis without managing external compute; (4) a Snowflake Data Marketplace listing makes the de-identified dataset publicly available to advertising companies regardless of their cloud provider or region, with no infrastructure to maintain. Options A and D are incomplete or use COPY INTO (not event-driven). Option C uses Amazon EMR/PySpark, which introduces significant infrastructure management overhead.
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