DAA-C01 Question 223
Single answerDomain 4.0: Data Presentation and Data Visualization (28%)A retail analytics team uses Snowsight dashboards to present daily sales KPIs to executives. The source data is stored in Snowflake tables and refreshed throughout the day by ELT pipelines. Executives want dashboard tiles to load quickly, but they also require the numbers to be no more than 15 minutes old. The analyst notices that some charts are slow because each tile runs complex aggregations over large fact tables. Which approach should the analyst take to best balance dashboard performance with the freshness requirement?
- A
Create a dynamic table that pre-aggregates the dashboard metrics and set an appropriate target lag of 15 minutes, then point the dashboard queries to the dynamic table.
- B
Create a standard view on top of the fact tables and rely on Snowsight chart caching so that the dashboard always loads quickly while staying within the 15-minute freshness requirement.
- C
Export the KPI data to a local CSV file every 15 minutes and upload the file to a Snowsight worksheet for visualization.
- D
Replace the dashboard queries with secure views because secure views are optimized for repeated dashboard access and automatically improve performance for aggregations.
Show answer and explanation
Correct answer: A
Explanation
For dashboard presentation in Snowsight, a common best practice is to separate expensive transformation logic from interactive consumption. When executives need fast tiles and reasonably fresh data, precomputing presentation-ready aggregates is typically more effective than querying large transactional or fact tables directly. In Snowflake, dynamic tables are well suited for this pattern because they maintain query results with an explicitly defined target lag, making them appropriate for near-real-time reporting layers. By contrast, standard views do not improve performance on their own because they are logical abstractions, not stored results. Secure views address data protection and governance requirements, not dashboard acceleration. Snowflake documentation on dynamic tables emphasizes maintaining query results with a target lag for downstream consumption, which aligns directly with the scenario's 15-minute freshness requirement. This makes the dynamic-table-based aggregation layer the best fit for balancing data presentation performance and freshness.
- A. Correct.
Correct. A dynamic table is designed to maintain the results of a query incrementally and can be configured with a target lag that aligns with freshness expectations. Pre-aggregating expensive dashboard metrics into a dynamic table reduces repeated computation at dashboard runtime and is a practical way to support near-real-time presentation requirements in Snowflake. Pointing Snowsight dashboards at the precomputed dataset typically improves responsiveness while keeping data within the defined lag window.
- B. Incorrect.
Incorrect. A standard view does not materialize or precompute results; it simply stores the query definition. Complex aggregations would still execute against the base fact tables when the dashboard runs. Also, relying on caching alone is not a dependable design choice for a defined freshness SLA, because cache use depends on query patterns and data changes rather than an explicit 15-minute maintenance target.
- C. Incorrect.
Incorrect. Exporting to CSV and manually or externally reloading for presentation is not a best-practice Snowflake solution for executive dashboards. It introduces operational overhead, weakens governance, and creates unnecessary data movement. It also does not leverage Snowflake-native mechanisms for maintaining fresh, queryable presentation layers.
- D. Incorrect.
Incorrect. Secure views are primarily for protecting sensitive logic and limiting exposure of underlying data structures. They do not exist as a performance optimization feature for dashboard workloads. In some cases, secure objects can even reduce optimization opportunities because Snowflake must preserve the security boundary.