ARA-C01 Question 131
Single answerUse casesA global retail company is redesigning its analytics platform in Snowflake. The platform must support three workloads: (1) a curated enterprise data warehouse used for finance and operations reporting, (2) a data science environment where teams explore large semi-structured clickstream data and build features, and (3) secure data distribution to external suppliers who should only see data products relevant to their region. The architects want to choose Snowflake-native patterns that align to each workload while minimizing data movement and operational overhead.
Which approach BEST matches these use cases?
- A
Build all workloads in a single shared schema, export subsets of supplier data to cloud storage for delivery, and let data scientists query the same reporting tables used by finance.
- B
Use relational star/snowflake modeling for the enterprise warehouse, store and process semi-structured clickstream data natively in Snowflake for data science exploration, and distribute curated supplier datasets through Secure Data Sharing or listings.
- C
Flatten all clickstream JSON into fully relational tables before landing it in Snowflake, replicate finance data into separate accounts for each internal team, and provide suppliers access through direct table grants.
- D
Use materialized views as the primary method for external supplier distribution, avoid semi-structured storage because it reduces query performance, and keep finance reporting in data lake files queried externally.
Show answer and explanation
Correct answer: B
Explanation
The best answer is Option 2 because it aligns Snowflake capabilities to the stated business use cases with minimal data movement. Snowflake is commonly used as an enterprise data warehouse for curated, governed reporting workloads using relational or dimensional models. It also supports data engineering and data science use cases by storing and querying semi-structured data natively through VARIANT columns and functions for JSON, Avro, Parquet, XML, and related formats. For external data distribution, Secure Data Sharing enables providers to share live data without copying it, and listings can support broader data product distribution patterns. These are all standard Snowflake architectural patterns and best practices for reducing duplication, preserving governance, and supporting mixed workloads efficiently.
- A. Incorrect.
Incorrect. Although a single shared schema may seem simpler initially, it does not align well with different workload isolation and governance needs. Exporting supplier data to cloud storage increases data movement and operational overhead compared with Snowflake-native sharing patterns. Letting data scientists rely only on the same reporting tables used by finance also limits flexibility for exploratory analysis on raw or semi-structured data and can create contention between workloads.
- B. Correct.
Correct. This option maps each workload to a well-established Snowflake use case. For the enterprise warehouse, dimensional or relational modeling is appropriate for governed reporting. For data science and clickstream exploration, Snowflake supports native storage and querying of semi-structured data such as JSON using VARIANT and related functions, reducing the need for early rigid transformation. For external suppliers, Secure Data Sharing and provider listings are purpose-built for distributing governed datasets without copying data, which minimizes movement and operational burden.
- C. Incorrect.
Incorrect. Flattening all JSON before landing is a common misconception; Snowflake is specifically designed to ingest and query semi-structured data natively, so full upfront flattening is not required and may remove flexibility. Replicating finance data into separate accounts for each internal team is usually unnecessary for internal workload separation and increases complexity. Direct table grants to suppliers are not the preferred pattern for external distribution across organizational boundaries; secure sharing is designed for that use case.
- D. Incorrect.
Incorrect. Materialized views can improve performance for specific query patterns, but they are not the primary mechanism for distributing data externally to suppliers. The statement about avoiding semi-structured storage is inaccurate because Snowflake has strong native support for semi-structured data. Keeping finance reporting in external data lake files rather than in governed Snowflake warehouse structures does not best fit the enterprise reporting use case described.