DAA-C01 Question 22
Select 2The elements that are required for business goals (using BI reports or SQL analysis)A retail analytics team is asked to deliver a new executive dashboard in Snowflake for the business goal: "Increase quarterly revenue from repeat customers in the online channel." The business users want the dashboard to be trusted for decision-making and comparable across regions. Before building BI reports or writing SQL, which TWO elements are most important to define to ensure the analysis aligns to the business goal?
- A
A precise KPI definition for repeat-customer revenue, including metric formula, time grain, and business rules such as how returns and canceled orders are handled
- B
The warehouse size that the BI tool should use for every dashboard query so executives get the fastest response time possible
- C
The dimensions and filters required for analysis, such as region, channel, quarter, and customer segment, with agreed definitions for each
- D
A requirement that all analysis be performed only with Snowsight charts instead of external BI tools to guarantee consistency
- E
A list of all raw source tables in the landing zone, even if they are not part of the business question
Show answer and explanation
Correct answers: A, C
Explanation
For business-goal-driven analytics in Snowflake, the most important starting point is defining what the business is trying to measure and how it should be analyzed. In practice, that means establishing clear metric/KPI definitions and the dimensions or filters used to evaluate those metrics. This aligns with standard analytics best practices: define the business question, define the measures, define the grain, and define the dimensions and business rules before building BI content or SQL logic. In Snowflake environments, these definitions can then be implemented consistently in views, semantic layers, governed datasets, or shared SQL logic used by Snowsight and external BI tools. Performance settings such as warehouse size, or implementation details such as raw landing tables, are secondary to ensuring the metric is correct and comparable. Snowflake documentation and best practices around analytic data modeling, secure governed data sharing, and BI integration emphasize consistent definitions and trusted data models as the foundation for accurate reporting.
- A. Correct.
Correct. To support a business goal with BI reports or SQL analysis, the team must first define the KPI unambiguously. Terms like "repeat customer," "revenue," and "quarterly" can be interpreted differently across teams. A strong definition includes the calculation logic, aggregation level, treatment of returns or canceled orders, and any inclusion/exclusion rules. Without this, different reports can produce different answers for the same business question.
- B. Incorrect.
Incorrect. Query performance matters operationally, but warehouse sizing is not one of the core business-definition elements required to align analysis to the business goal. Choosing a warehouse size too early can optimize execution speed without ensuring the metric itself answers the business question correctly.
- C. Correct.
Correct. Business analysis must also define the dimensions, slices, and filters needed to evaluate the KPI against the stated goal. For this scenario, region, channel, quarter, and customer segment are essential because executives want comparisons across regions and focus specifically on the online channel. Agreed dimensional definitions are necessary so BI reports and SQL outputs are consistent and actionable.
- D. Incorrect.
Incorrect. Snowflake supports analysis through Snowsight, SQL, and many partner BI tools. Tool choice does not by itself guarantee analytical consistency. Consistency comes from shared semantic definitions, governed metrics, and common business rules rather than forcing a single visualization interface.
- E. Incorrect.
Incorrect. Knowing source systems can help with lineage and implementation, but listing all raw landing-zone tables is not a required element for defining the business goal. This option reflects a common mistake of starting from raw data assets rather than first clarifying the KPI and analytical dimensions needed by stakeholders.