ARA-C01 Question 119
Select 22.1 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: (1) high-volume point-of-sale transactions that must be loaded continuously and queried for operational dashboards, (2) product catalog data with nested and changing attributes from multiple suppliers, and (3) ad hoc analytics by business users who want a simple, consistent reporting layer. The architect wants to choose data models in Snowflake that balance flexibility, query performance, and ease of use. Which TWO design decisions are the most appropriate?
- A
Store supplier product attributes in a VARIANT column in a curated table, and expose commonly used fields through relational views for analysts.
- B
Model all data, including product attributes, in a fully normalized 3NF enterprise schema and require analysts to query base tables directly to maximize flexibility.
- C
Use a dimensional model for the business reporting layer over sales data, while keeping raw and semi-structured source data in landing/curated layers for ingestion flexibility.
- D
Flatten all semi-structured product data into a very wide relational table during ingestion because Snowflake cannot efficiently query nested data in VARIANT columns.
- E
Use a single denormalized table for all operational and analytic workloads because Snowflake storage and compute separation removes most data modeling trade-offs.
Show answer and explanation
Correct answers: A, C
Explanation
The best answer is to combine semi-structured and dimensional modeling based on workload needs. In Snowflake, VARIANT is valuable for ingesting and storing data with evolving or nested structures, such as supplier product attributes, because it minimizes friction from schema drift. However, BI users typically benefit from a simpler relational or dimensional presentation layer. For sales reporting and ad hoc analytics, a dimensional model is often more effective than exposing normalized base tables directly, because it reduces query complexity and aligns with common analytics patterns. Snowflake documentation emphasizes support for structured and semi-structured data, including querying nested elements in VARIANT, and common best practice is to separate raw ingestion structures from curated consumption models. The key architectural principle is that Snowflake supports multiple data models, and architects should choose each model according to workload requirements rather than assuming one model fits all use cases.
- A. Correct.
Correct. Snowflake supports semi-structured data natively through VARIANT, OBJECT, and ARRAY types, which is well suited for product attributes that vary by supplier and evolve over time. Keeping those attributes in VARIANT preserves flexibility and reduces the need for constant schema changes during ingestion. Exposing frequently used attributes through relational views or curated tables improves usability for BI users and can simplify governance and semantic consistency. This reflects a common Snowflake pattern: retain semi-structured fidelity in raw/curated layers while presenting a more relational interface for consumption.
- B. Incorrect.
Incorrect. A fully normalized 3NF model can be appropriate for some integrated enterprise data stores, but requiring analysts to query many base tables directly usually hurts ease of use and can increase query complexity. In Snowflake, dimensional models are often preferred for business reporting because they simplify joins and align better with BI tools and user expectations. The misconception here is that maximum normalization automatically maximizes flexibility for all workloads; in practice, it can reduce analyst productivity and is not ideal as the primary reporting interface.
- C. Correct.
Correct. This is a strong fit for the stated requirements. Dimensional modeling is commonly used in Snowflake for curated reporting and ad hoc analytics because it offers a user-friendly structure, predictable joins, and good performance characteristics for analytic queries. At the same time, retaining raw and semi-structured data in earlier layers supports fast ingestion and schema evolution. This approach recognizes that different workloads benefit from different models rather than forcing one model across the entire platform.
- D. Incorrect.
Incorrect. Snowflake can query data stored in VARIANT and can project nested elements using path notation and functions. Flattening every attribute into a very wide table at ingestion may create unnecessary schema churn, sparse columns, and maintenance overhead, especially when supplier attributes change frequently. The misconception is that semi-structured data must be fully relationalized immediately for performance or accessibility; Snowflake is specifically designed to work with semi-structured data without that requirement.
- E. Incorrect.
Incorrect. Separation of storage and compute is a major Snowflake architectural benefit, but it does not eliminate data-modeling trade-offs. A single denormalized table for all operational and analytic workloads can create governance, maintainability, and usability problems, especially when combining highly variable product attributes with transactional facts. Different workloads still benefit from different logical models, such as semi-structured ingestion models and dimensional reporting models.