SOL-C01 Question #243: Real Exam Question with Answer & Explanation

Question

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?

Options

• AUse Snowflake external functions to call a separate SageMaker endpoint for model inference and
• BUtilize Snowflake's Snowpark ML to deploy and manage models directly within Snowflake,
• CCreate a separate Spark cluster to process Kafka streams and use the Snowflake Spark connector
• DEmploy Snowflake's Snowpipe for Kafka integration, use Snowflake Tasks to schedule data
• EUse Snowflake's Streamlit integration for real-time data visualization of Kafka streams, and use

Explanation

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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