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Snowflake

ARA-C01 · Question #83

An Architect needs to automate the daily Import of two files from an external stage into Snowflake. One file has Parquet-formatted data, the other has CSV-formatted data. How should the data be…

The correct answer is B. Create a task using Snowflake scripting that will import the files, and then call a User-Defined. To automate a daily import of two files (Parquet and CSV) from an external stage and join/aggregate the results, a Snowflake Task using Snowflake Scripting is the correct approach (Option B). Tasks support scheduling (e.g., CRON-based daily triggers) and Snowflake Scripting…

Data Engineering

Question

An Architect needs to automate the daily Import of two files from an external stage into Snowflake. One file has Parquet-formatted data, the other has CSV-formatted data. How should the data be joined and aggregated to produce a final result set?

Options

  • AUse Snowpipe to ingest the two files, then create a materialized view to produce the final result
  • BCreate a task using Snowflake scripting that will import the files, and then call a User-Defined
  • CCreate a JavaScript stored procedure to read. join, and aggregate the data directly from the
  • DCreate a materialized view to read, Join, and aggregate the data directly from the external stage,

How the community answered

(46 responses)
  • A
    4% (2)
  • B
    85% (39)
  • C
    9% (4)
  • D
    2% (1)

Explanation

To automate a daily import of two files (Parquet and CSV) from an external stage and join/aggregate the results, a Snowflake Task using Snowflake Scripting is the correct approach (Option B). Tasks support scheduling (e.g., CRON-based daily triggers) and Snowflake Scripting provides imperative control to orchestrate the COPY INTO operations for both file formats sequentially, then call a User-Defined Table Function (UDTF) or stored procedure to join and aggregate the data. Option A is incorrect because Snowpipe is designed for continuous micro-batch ingestion, not scheduled batch imports, and materialized views cannot source from external stages. Option D is incorrect because materialized views cannot be defined on external stages. Option C (JavaScript stored procedure) can work but is less ideal than native Snowflake Scripting with Tasks for this orchestration pattern.

Topics

#Data Ingestion#Data Pipelines#Automation#Snowflake Scripting

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