DEA-C02 Exam Dumps | A Snowflake data pipeline ingests data from multiple external sources into a RAW DATA table. A transformation

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Question 56/149

A Snowflake data pipeline ingests data from multiple external sources into a RAW DATA table. A transformation process then moves the data to a ANALYTICS DATA table, applying several complex UDFs written in Java and Python for data cleansing and enrichment. Performance is significantly slower than expected. Which combination of techniques would BEST improve the performance of this transformation pipeline?

A. Increase the virtual warehouse size and re-cluster the ANALYTICS DATA table based on the most frequently filtered columns.

B. Rewrite the UDFs in SQL or Snowpark Python/Java for better integration with the Snowflake engine and leverage vectorization where possible; cache intermediate results using temporary tables.

C. Use external functions instead of UDFs to offload the processing to an external compute environment and configure auto-scaling for the virtual warehouse.

D. Implement data partitioning in the RAW DATA table based on ingestion time and switch to using stored procedures instead of transformation pipelines.

E. Reduce the number of UDF calls by consolidating them into a single, more complex UDF. Replace the transformation pipeline with a series of COPY INTO statements.

Correct Answer: B

Rewriting UDFs in SQL or Snowpark allows the Snowflake engine to optimize them more effectively. Using temporary tables to cache intermediate results prevents redundant computation. Option A helps, but addressing UDF performance is more crucial. Option C might add complexity. Option D is irrelevant to UDF performance. Option E is counterproductive; consolidating UDFs can reduce parallelism and COPY INTO is for initial data loading, not transformation.

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