SnowPro Associate: Platform Question 4
Single answer1.1 Outline key features and benefits of the Snowflake AI Data Cloud.A retail company wants to modernize its analytics platform. The data engineering team needs to ingest semi-structured clickstream data, the finance team wants to run reporting workloads without being slowed down by ETL jobs, and the business wants to share selected datasets with a strategic supplier without copying files to external systems. Which Snowflake AI Data Cloud capability best addresses all of these requirements?
- A
Use separate virtual warehouses for different workloads, store semi-structured data natively, and share governed data securely through Snowflake Secure Data Sharing
- B
Deploy a single large virtual warehouse for all users, convert all semi-structured data to relational format before loading, and export shared data daily to cloud object storage
- C
Use external tables as the primary storage for all data, require each department to maintain its own copy of shared datasets, and use a third-party ETL tool for all cross-team access
- D
Load all data into temporary tables for faster processing, run reporting and ETL in the same warehouse to simplify management, and share data by granting direct access to internal stage files
Show answer and explanation
Correct answer: A
Explanation
This question tests practical understanding of key Snowflake AI Data Cloud platform benefits: native handling of structured and semi-structured data, separation of storage and compute, independent scaling of compute through virtual warehouses, and secure live data sharing without copying data. In real-world implementations, teams often use separate warehouses to isolate workloads and improve performance for concurrent users. Snowflake also enables querying semi-structured formats such as JSON, Avro, ORC, Parquet, and XML. For collaboration, Secure Data Sharing allows providers to share live data with consumers while maintaining governance and avoiding data duplication. These capabilities are consistently emphasized in Snowflake product documentation and SnowPro Associate exam objectives around core platform value and architecture.
- A. Correct.
Correct. This option combines three core Snowflake AI Data Cloud benefits: support for semi-structured data such as JSON without requiring full transformation before loading, workload isolation through independent virtual warehouses so reporting and ETL do not compete for compute resources, and Secure Data Sharing to share live governed data without copying or moving files. These are foundational Snowflake platform capabilities and align directly to the scenario.
- B. Incorrect.
Incorrect. A single large warehouse does not address workload isolation as effectively as separate warehouses, because all workloads still contend within the same compute cluster. Requiring all semi-structured data to be converted before loading removes one of Snowflake's advantages: native support for semi-structured data. Exporting data to object storage for sharing introduces unnecessary data movement and governance challenges, which Snowflake Secure Data Sharing is designed to avoid.
- C. Incorrect.
Incorrect. External tables are useful for querying data in external storage, but they are not the best answer for primary storage of all platform data in this scenario. Requiring each department to maintain its own copy of datasets contradicts Snowflake's data sharing and centralized data platform benefits. Using third-party ETL for all access is not necessary to solve the sharing and workload isolation requirements described.
- D. Incorrect.
Incorrect. Temporary tables are session-scoped and are not an enterprise data sharing strategy. Running reporting and ETL in the same warehouse increases the risk of resource contention, which Snowflake's multi-cluster, multi-warehouse architecture helps avoid. Sharing by granting access to internal stage files is not the recommended governed sharing model for business data; Secure Data Sharing is the appropriate capability.