AWS Certified Machine Learning Engineer - Associate MLA-C01 — Topic 1 Question 226

Question

An ML engineer is designing an AI-powered traffic management system to adjust traffic lights during predicted congestion. The system must use near real-time inference to generate predictions to help prevent traffic collisions. The system must use a batch processing pipeline to perform historical analysis of the predictions to continuously refine and improve the model. The historical analysis will take several hours to evaluate how well the predictions correlate with actual outcomes. The system must be able to scale inference endpoints appropriately to meet demand. Which combination of solutions will meet these requirements? (Choose two.)

Accepted Answer

Correct answer: A, C. The correct answer is A, C. Option A: Use Amazon SageMaker real-time inference endpoints. Configure the endpoints to scale automatically based on a target tracking scaling policy that uses the metric ConcurrentInvocationsPerinstance. Option C: Configure an Amazon SageMaker Processing job for batch analysis of historical prediction data. Use Amazon EventBridge to schedule the job to run daily. Allow several hours for in-depth analysis to refine and improve t... AWS Batch schedules and runs batch computing jobs at any scale without managing clusters. Amazon EventBridge routes events between services with rules, enabling event-driven, decoupled architectures. Amazon SageMaker builds, trains and deploys machine learning models in a fully managed service. This option meets the real-time / low-latency performance requirement.

AWS Certified Machine Learning Engineer - Associate MLA-C01 — Question 226