AWS Certified Machine Learning – SpecialtyMachine Learning Implementation and OperationsEasy
A machine learning engineer is deploying a real-time inference endpoint for a critical fraud detection model using Amazon SageMaker. The model requires high availability and needs to be resilient to instance failures within a single Availability Zone. Which configuration should the engineer primarily focus on to address this requirement?
- AImplementing Elastic Inference
- BConfiguring a Multi-AZ endpoint
- CEnabling SageMaker Model Monitor
- DSetting up SageMaker Pipelines
Show answer & explanationAnswer & explanation
Correct answer: B. Configuring a Multi-AZ endpoint
Configuring a Multi-AZ endpoint for Amazon SageMaker ensures that the inference endpoint is deployed across multiple Availability Zones. This provides high availability and resilience against single Availability Zone outages or instance failures within a single AZ.
Why the other options are wrong
- A. Elastic Inference accelerates inference performance by attaching GPU-powered acceleration, not primarily for high availability.
- C. SageMaker Model Monitor is for detecting data and model quality drift, not for high availability.
- D. SageMaker Pipelines are for orchestrating ML workflows, not for ensuring real-time endpoint availability.
SageMaker Multi-AZ Endpoint
An Amazon SageMaker endpoint configuration that deploys model instances across multiple AWS Availability Zones to ensure high availability and fault tolerance.
- Increases resilience against AZ failures.
- Distributes traffic across zones.
- Crucial for critical, high-availability ML applications.
Memory trick: Multiple Zones, Multiple Guards.