Leverage Snowflake AI/ML Capabilities to Enhance Security Posture SNOWPRO-ADVANCED-SECURITY-ENGINEER Practice Questions (Page 3)

Questions 11–15

1. 11foundation · easy

   Which of the following is an external integration that can be used with Snowflake to enhance AI/ML security monitoring?

   • ASnowflake Data Sharing
   • BSnowflake Cortex
   • CAmazon SageMaker
   • DSnowflake ML Functions
   Reveal Solution Discussion Select an answer first
2. 12application · medium

   A security team wants to build a system that detects and responds to threats in real-time. They plan to use Snowflake for data storage and analysis, and they want to use a pre-built ML model for anomaly detection. They also want to integrate with an external orchestration tool to automate responses. Which combination of Snowflake features should they use?

   • ASnowpipe, SNOWFLAKE.ML.FORECAST, and tasks.
   • BSnowflake Cortex, SNOWFLAKE.ML.ANOMALY_DETECTION, and external functions.
   • CData sharing, SNOWFLAKE.ML.ANOMALY_DETECTION, and stored procedures.
   • DMaterialized views, SNOWFLAKE.ML.CLASSIFICATION, and streams.
   Reveal Solution Discussion Select an answer first
4. 13foundation · easy

   Which of the following is an example of integrating Snowflake with an external AI/ML platform for security monitoring?

   • AUsing Snowflake's built-in ML functions
   • BUsing Snowflake's Snowpipe for data ingestion
   • CUsing Snowflake's data sharing to share data with partners
   • DConnecting Snowflake to Amazon SageMaker for model training
   Reveal Solution Discussion Select an answer first
5. 14foundation · easy

   What is a primary reason to audit AI/ML workloads in Snowflake?

   • ATo ensure compliance and detect misuse
   • BTo increase data sharing
   • CTo reduce storage costs
   • DTo improve query performance
   Reveal Solution Discussion Select an answer first
6. 15expert · hard

   A security team has trained a classification model to detect phishing emails. The model is stored in Snowflake. They need to deploy it so that the email gateway can call the model to classify each email in real-time. The email gateway is an external system. What is the most secure and efficient way to expose the model to the email gateway?

   • AShare the model with the email gateway using a data share.
   • BCreate an external function that wraps the model inference call and grant the email gateway's service account USAGE on the external function.
   • CExport the model as a SQL file and provide it to the email gateway to run locally.
   • DCreate a stored procedure that the email gateway can call directly.
   Reveal Solution Discussion Select an answer first