DAA-C01 Question 37
Single answerFind external data sets that correlate with available dataA retail analytics team stores daily in-store sales by ZIP code in Snowflake and wants to improve forecasting by adding external data that correlates with local demand. The analysts want a solution that minimizes data engineering effort, allows them to evaluate providers before committing, and enables joining the external data with their existing ZIP code-based sales tables. Which approach should they take?
- A
Use Snowflake Marketplace to discover third-party datasets that include geographic attributes such as ZIP code, request or access a listing, and evaluate the data directly in Snowflake before joining it to internal sales tables.
- B
Export the sales data to a local workstation, search for public CSV files on the internet, and manually compare schemas before reloading any useful files into Snowflake.
- C
Create an external function that calls a weather website for each ZIP code at query time, because external functions are the primary way to discover and assess third-party datasets.
- D
Use Snowsight only to build a dashboard of current sales trends, because Snowsight automatically recommends and provisions correlated external datasets for forecasting.
Show answer and explanation
Correct answer: A
Explanation
The best answer is to use Snowflake Marketplace to find external datasets that can be correlated with existing internal data, especially when the team wants low engineering overhead and the ability to evaluate datasets before broader adoption. In real-world analytics work, the key is not just obtaining external data, but obtaining it in a governed, easily consumable form with joinable attributes such as ZIP code, census geography, region, or timestamp. Snowflake Marketplace and provider listings support this pattern by allowing consumers to discover and access data directly in Snowflake without building and maintaining ingestion pipelines. After access is granted, analysts can profile the data, test joins, and measure whether variables such as weather, demographics, mobility, or local economic activity improve forecasting. This aligns with Snowflake best practices around secure data sharing and data collaboration. Relevant Snowflake documentation includes Snowflake Marketplace and provider/consumer listings documentation, as well as guidance on secure data sharing and using shared data directly in Snowflake for analytics.
- A. Correct.
Correct. Snowflake Marketplace is designed to help consumers find, access, and use third-party and shared datasets within Snowflake. For this scenario, the team can look for datasets with geographic dimensions such as ZIP code, demographics, weather, foot traffic, or economic indicators that can be joined to existing sales data. This approach minimizes engineering effort because the data can be accessed directly in Snowflake rather than requiring custom ingestion pipelines. It also supports evaluation before commitment, depending on provider terms and available listings, which aligns with the requirement to assess correlation and usefulness before operationalizing it.
- B. Incorrect.
Incorrect. While analysts could manually search for external CSV files, this approach increases data engineering overhead, introduces governance and quality risks, and does not align with the requirement to minimize effort. It also moves data outside Snowflake unnecessarily. A common misconception is that finding correlated external data is primarily a file-ingestion problem; in Snowflake, curated data discovery through Marketplace or data sharing is typically the more efficient and governed approach.
- C. Incorrect.
Incorrect. External functions can call remote services from Snowflake, but they are not the primary mechanism for discovering, previewing, or subscribing to external datasets. Calling a weather website row by row for each ZIP code at query time would also likely be inefficient, operationally brittle, and dependent on API behavior and latency. This option reflects the misconception that any external data need should be solved with runtime API calls rather than using governed, share-based datasets where possible.
- D. Incorrect.
Incorrect. Snowsight is useful for exploration, querying, and visualization, but it does not automatically recommend and provision third-party datasets for correlation analysis. Analysts can use Snowsight to inspect and analyze data once it is available, but dataset discovery and access are typically handled through Snowflake Marketplace, listings, and data sharing workflows. This distractor targets the misconception that BI and UI tooling replaces the underlying data acquisition process.