The Data Engineering team at a large manufacturing company needs to engineer data coming from many sources to support a wide variety of use cases and data consumer requirements which include: 1)…

Question

Options

• AConsolidate data in the company's data lake and use EXTERNAL TABLES.
• BCreate a raw database for landing and persisting raw data entering the data pipelines.
• CCreate a set of profile-specific databases that aligns data with usage patterns.
• DCreate a single star schema in a single database to support all consumers' requirements.
• ECreate a Data Vault as the sole data pipeline endpoint and have all consumers directly access the

• A
  10% (6)
• B
  67% (39)
• D
  19% (11)
• E
  3% (2)

Explanation

This question tests knowledge of Snowflake data modeling patterns for multi-consumer environments. Option B (raw database for landing raw data) directly serves the Data Science team's need for unaltered data for ML model development, while also acting as the foundational ingestion layer for all pipelines. Option C (profile-specific databases aligned with usage patterns) is the standard Snowflake architectural pattern: Finance/Vendor Management gets reporting-ready, structured data; Data Science retains access to raw data; Sales gets engineered and masked/protected data for monetization - each consumer group gets a database shaped for their access pattern. Option A (external tables over a data lake) adds unnecessary complexity and latency without meeting the governance and engineering requirements. Option D (single star schema) cannot simultaneously serve raw ML needs and protected monetization needs in one schema. Option E (Data Vault as sole endpoint) forces all consumers to query a complex vault model, which is inappropriate for direct reporting or raw ML access.

No community discussion yet for this question.