DAA-C01 Exam Dumps | You are designing a data pipeline in Snowflake that ingests data from multiple external sources with

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Question 3/151

You are designing a data pipeline in Snowflake that ingests data from multiple external sources with varying schemas and data quality. After ingestion, you need to standardize the data format, handle missing values, and perform data type conversions before loading it into your analytical tables. You need to implement a reusable and maintainable solution. Which approach minimizes code duplication and maximizes data quality?

A. Create separate SQL scripts for each data source to handle the specific data cleaning and transformation requirements.

B. Implement a centralized stored procedure that accepts the data source name as a parameter and performs all data cleaning and transformation logic based on conditional statements (CASE statements).

C. Use Snowflake's pipes and Snowpipe to load raw data into staging tables, then use a combination of dynamic SQL, user-defined functions (UDFs), and stored procedures to perform the data cleaning and transformation in a modular and reusable manner.

D. Use Snowflake's external tables to directly query the data in its raw format and perform the data cleaning and transformation on-the-fly during query execution.

E. Ingest the data into a single large table without any transformation, and rely on business intelligence tools to handle data cleaning and transformation during analysis.

Correct Answer: C

Option C is the most robust and maintainable approach. It leverages Snowflake's features (pipes, Snowpipe, dynamic SQL, UDFs, and stored procedures) to create a modular and reusable data pipeline. Pipes and Snowpipe handle data ingestion efficiently. Dynamic SQL allows for building flexible queries based on metadata. UDFs encapsulate reusable data transformation logic. Stored procedures orchestrate the entire process. Options A and B lead to code duplication and are difficult to maintain. Option D can be inefficient for complex transformations. Option E pushes data quality issues to the BI layer, which is not ideal.

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