DEA-C02 Exam Dumps | A data engineering team uses Snowflake to analyze website clickstream data stored in AWS S3. The data

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Snowflake.DEA-C02.v2025-10-09.q149
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Question 77/149

A data engineering team uses Snowflake to analyze website clickstream data stored in AWS S3. The data is partitioned by year and month in the S3 bucket. They need to query the data frequently for reporting purposes but don't want to ingest the entire dataset into Snowflake due to storage costs and infrequent full dataset analysis. Which approach is the MOST efficient and cost-effective way to enable querying of this data in Snowflake?

A. Create a Snowflake internal stage, copy the necessary files into the stage, and then load the data into a Snowflake table.

B. Create a Snowflake external stage pointing to the S3 bucket, define an external table on the stage, and use partitioning metadata to optimize queries.

C. Load all the data into a Snowflake table and create a materialized view on top of the table to pre-aggregate the data for reporting.

D. Use Snowflake's COPY INTO command to ingest data directly from S3 into a Snowflake table on a scheduled basis.

E. Create a Snowpipe pointing to the S3 bucket and ingest the data continuously into a Snowflake table.

Correct Answer: B

Using an external table pointing to the S3 bucket is the most efficient and cost-effective approach. It allows you to query the data directly in S3 without ingesting it into Snowflake, saving on storage costs. Partitioning metadata further optimizes query performance by allowing Snowflake to only scan relevant partitions based on the query criteria.

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