SOL-C01 Exam Dumps | A data engineer is tasked with designing a Snowflake solution for a financial services company that needs

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Snowflake.SOL-C01.v2026-01-13.q155
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Question 96/155

A data engineer is tasked with designing a Snowflake solution for a financial services company that needs to perform real-time fraud detection. The solution needs to leverage external models trained in Python using Snowpark ML and ingest data from various sources, including Kafka streams and S3 buckets. Which of the following architectural choices would best leverage the Snowflake A1 Data Cloud capabilities for this scenario?

A. Use Snowflake external functions to call a separate SageMaker endpoint for model inference and load data from Kafka using Snowpipe and S3 using COPY INTO.

B. Utilize Snowflake's Snowpark ML to deploy and manage models directly within Snowflake, integrate with Kafka using Snowflake Kafka connector, and use external tables for S3 data access.

C. Create a separate Spark cluster to process Kafka streams and use the Snowflake Spark connector to write the processed data to Snowflake. Use COPY INTO for S3 data, and deploy the ML model using Snowflake external functions calling a Databricks cluster.

D. Employ Snowflake's Snowpipe for Kafka integration, use Snowflake Tasks to schedule data loading from S3, and leverage Snowflake's native SQL capabilities for fraud detection without employing external models.

E. Use Snowflake's Streamlit integration for real-time data visualization of Kafka streams, and use Snowpark ML to deploy and manage models directly within Snowflake, loading data from S3 via COPY INTO.

Correct Answer: B,E

Option B and E offer the best solutions. Option B leverages Snowflake's Snowpark ML for model management within Snowflake, Snowflake Kafka connector for direct Kafka integration, and external tables to avoid data duplication for S3. Option E complements B by adding Streamlit integration for Real Time Visualization of Kafka Streams. Option A, C and D introduce unnecessary external dependencies (SageMaker, Spark, Databricks) and don't fully utilize Snowflake's native capabilities for data processing and ML model deployment. Choosing native capabilities minimizes complexity and operational overhead.

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