AWS Certified Machine Learning Engineer - Associate MLA-C01 — Topic 1 Question 207

Question

An airline company uses an ML model to adjust ticket prices based on demand. The model runs on Amazon SageMaker real-time endpoints. During previous deployments, the model failed to scale quickly enough when website traffic increased, which caused delays in price adjustments. An ML engineer needs to configure auto scaling for the SageMaker endpoints to respond rapidly to traffic changes. The solution must use target tracking scaling policies. Which configuration will be MOST responsive to sudden changes in traffic?

Accepted Answer

Correct answer: D. The correct answer is D. Option D: Configure auto scaling based on the SageMaker InvocationsPerInstance metric. Configure high-resolution 10-second intervals, and set the default 300-second scale-in cooldown period. Auto Scaling adjusts capacity automatically to match demand, improving availability and cost efficiency. Amazon SageMaker builds, trains and deploys machine learning models in a fully managed service. AWS Config tracks resource configuration changes and evaluates compliance. This option meets the real-time / low-latency performance requirement.

AWS Certified Machine Learning Engineer - Associate MLA-C01 — Question 207