ARA-C01 Question 118
Single answer2.1 Outline the benefits and limitations of various data models in a Snowflake environment.A retail company is redesigning its Snowflake data platform to support three workloads from the same source data: (1) executive dashboards with stable, high-concurrency reporting requirements, (2) ad hoc analyst exploration across many business dimensions, and (3) ingestion of rapidly evolving product catalog attributes from multiple vendors, where each vendor supplies different fields and nested structures. The architects want to minimize downstream rework while still delivering predictable reporting performance. Which design approach BEST balances the benefits and limitations of data models in Snowflake for this scenario?
- A
Store all source data in a single highly normalized 3NF model, including vendor-specific attributes flattened into relational tables, because normalized models provide the best query performance for both dashboards and ad hoc analytics in Snowflake.
- B
Use a dimensional model for curated reporting and analyst-facing consumption layers, while retaining semi-structured VARIANT columns in a raw or integration layer for vendor-specific product attributes that do not conform well to a fixed schema.
- C
Convert all data into a wide single table with one column for every possible product attribute across all vendors, because Snowflake performs best when joins are eliminated even if most columns are sparse and frequently changing.
- D
Model the dashboard layer directly on top of raw semi-structured JSON in VARIANT columns, because Snowflake automatically optimizes semi-structured data to the same usability and governance level as a curated dimensional model.
Show answer and explanation
Correct answer: B
Explanation
The best answer is to combine data models based on workload requirements rather than forcing one model to serve every purpose. In Snowflake, dimensional models are commonly preferred for curated analytics and dashboard consumption because they simplify access patterns and are easier for BI tools and end users to understand. Semi-structured data support through VARIANT is valuable when source attributes are heterogeneous, nested, or rapidly changing, since it avoids constant schema refactoring during ingestion. A normalized model may still have a role in integration layers, but it is not typically the optimal primary model for all analytic consumption. Snowflake documentation on semi-structured data emphasizes native support for storing and querying JSON, Avro, ORC, Parquet, and XML through VARIANT and related functions. Snowflake best practices for analytics also commonly favor curated, consumer-friendly models such as star schemas or other dimensional designs for reporting and self-service use cases.
- A. Incorrect.
Incorrect. A normalized 3NF model can be useful for integrating operational data and reducing redundancy, but it is not typically the best fit for high-concurrency BI dashboards and broad analyst self-service. Those workloads often benefit from simpler dimensional structures that reduce join complexity and improve usability. Also, forcing highly variable vendor-specific nested attributes into rigid relational tables can create excessive schema churn and ETL complexity. In Snowflake, normalized models are supported, but they are not inherently the best-performing or easiest model for all analytic use cases.
- B. Correct.
Correct. This approach aligns the model to the workload. Dimensional modeling is well suited for stable reporting and interactive analytics because facts and dimensions are easier for BI tools and analysts to consume. At the same time, Snowflake's support for semi-structured data through VARIANT allows the platform to retain vendor-specific and evolving attributes without repeatedly redesigning relational schemas. This balances flexibility in ingestion with governed, performant consumption models downstream.
- C. Incorrect.
Incorrect. A single wide table may reduce some joins, but it introduces major limitations in this scenario: sparse columns, ongoing DDL changes as new vendor attributes appear, poor semantic organization, and potential governance challenges. It also does not handle nested or irregular semi-structured content as naturally as VARIANT. Snowflake can query wide tables, but using one massive denormalized table for all use cases is usually not the best architectural choice when attribute sets change frequently.
- D. Incorrect.
Incorrect. Snowflake provides strong support for querying semi-structured data, including JSON stored in VARIANT, but that does not make raw semi-structured data equivalent to a curated dimensional model for reporting. BI dashboards usually require conformed definitions, stable schemas, clear business keys, and governed metrics. Building dashboards directly on raw JSON can increase complexity for users, reduce consistency, and make performance tuning and semantic standardization more difficult.