ARA-C01 Question 130
Single answerUse casesA global retailer is modernizing its analytics platform on Snowflake. The architecture team must support three distinct workloads: (1) near-real-time operational dashboards for supply chain teams, (2) data science experimentation on large historical datasets with Python-based model training, and (3) secure data sharing of curated sales data with external suppliers without copying data out of Snowflake. The company wants to minimize operational overhead while using native Snowflake capabilities where they are the best fit. Which Snowflake use-case mapping is the MOST appropriate?
- A
Use Snowpipe Streaming or Snowpipe for low-latency ingestion into Snowflake for dashboards, Snowpark for Python-based data science processing, and Secure Data Sharing for external suppliers
- B
Use database replication for low-latency dashboard updates, external tables for Python model training inside Snowflake, and reader accounts as the only supported way to share data with suppliers
- C
Use batch COPY INTO once per day for operational dashboards, standard SQL UDFs for all Python-based model training, and unload curated data to cloud storage for supplier access
- D
Use materialized views as the ingestion mechanism for operational dashboards, Apache Iceberg tables as the required engine for Python experimentation, and cross-region failover groups for supplier data distribution
Show answer and explanation
Correct answer: A
Explanation
The best answer is Option 1 because it maps each business need to the most appropriate native Snowflake use case with minimal operational overhead. Near-real-time dashboards generally require continuous or low-latency ingestion, for which Snowpipe and Snowpipe Streaming are the relevant Snowflake patterns. Python-based data science workloads are well served by Snowpark for Python, which allows developers to process data in Snowflake without unnecessary data movement. For external distribution of curated data without creating copies, Secure Data Sharing is Snowflake's core capability, enabling live sharing with governance controls.
The distractors reflect common architectural misconceptions: using replication instead of ingestion pipelines, treating external tables as a primary data science execution framework, assuming reader accounts are the only sharing model, relying on daily batch loads for operational use cases, or confusing performance, interoperability, and disaster recovery features with workload delivery patterns. Snowflake documentation consistently distinguishes these use cases: Snowpipe/Snowpipe Streaming for ingestion, Snowpark for developer and data science workloads, Secure Data Sharing for governed sharing, replication/failover groups for business continuity, and materialized views for query optimization.
- A. Correct.
Correct. This option aligns each workload to a well-established Snowflake use case. For near-real-time dashboards, Snowpipe or Snowpipe Streaming is appropriate for continuous, low-latency ingestion. For Python-based data science on historical datasets, Snowpark supports Python development and pushes processing closer to the data in Snowflake, which is a common architectural choice. For sharing curated data externally without copying it out of Snowflake, Secure Data Sharing is the native capability specifically designed for live, governed sharing of Snowflake data.
- B. Incorrect.
Incorrect. Database replication is intended for business continuity, disaster recovery, and cross-region/account synchronization scenarios, not as a primary low-latency ingestion method for dashboard freshness. External tables can expose data in cloud storage, but they are not the primary feature for Python model training 'inside Snowflake'; Snowpark is better aligned to that use case. Reader accounts are one way to share data with consumers who do not already have a Snowflake account, but they are not the only supported sharing mechanism. Direct Secure Data Sharing to another Snowflake account is also supported and often preferred.
- C. Incorrect.
Incorrect. Once-daily batch COPY INTO does not satisfy a near-real-time operational dashboard requirement. Standard SQL UDFs are not a substitute for end-to-end Python-based model training workflows; Snowpark for Python is the more appropriate native capability. Unloading curated data to cloud storage creates copies, adds operational overhead, and loses many governance and live-sharing benefits that Secure Data Sharing is designed to provide.
- D. Incorrect.
Incorrect. Materialized views improve query performance for repeated query patterns but are not an ingestion mechanism. Apache Iceberg support in Snowflake is relevant for open table interoperability scenarios, but it is not a required engine for Python experimentation or model training. Failover groups are used for disaster recovery and business continuity across accounts/regions, not for distributing shared datasets to suppliers as a primary data-sharing pattern.