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MLA-C02 practice question: Inference cost and capacity (1 of 2)

Operating, Monitoring, and Securing ML and AI Solutions A free question from our original MLA-C02 practice exam. Try it, then open the answer to see why each option is right or wrong.

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A dental chain serves an X-ray triage image model on GPU instances in SageMaker AI. Traffic is steady, and the model is a standard convolutional network trained and validated in TensorFlow. The finance team wants the inference cost cut without rewriting the model.

Inference latency must stay within the current service target, and the team is willing to compile the model for new target hardware as a one-time step.

Which change best meets these requirements?

Choose one answer.

  1. A. Move the endpoint to a larger GPU instance type, so that each request finishes sooner and the endpoint is billed for fewer instance hours.
  2. B. Replace the model with a rule-based image threshold that requires no model at all, since rules never cost money to serve.
  3. C. Turn off all logging and monitoring on the endpoint, which removes the cost of the logs without changing the model.
  4. D. Compile the model with SageMaker Neo for an AWS Inferentia instance type, and deploy the compiled model to an Inferentia-based endpoint that meets the latency target.
Show the answer and explanation

Answer: D

  • A. Wrong: A larger instance costs more per hour, and the total hours do not necessarily fall for a steady workload. The option raises cost rather than cutting it.
  • B. Wrong: Rules may not match the accuracy of the model for X-ray triage, and the stem asks to keep the model without rewriting it. The option changes the service.
  • C. Wrong: Logging is a small share of the cost, and removing monitoring weakens the operations the team relies on. The option does not address the instance cost that drives the bill.
  • D. Right: Correct. Inferentia instances offer lower inference cost for supported models, and SageMaker Neo compiles a validated model for that hardware. The one-time compile step matches the team's willingness to do it. Learn more: Model performance optimization with SageMaker Neo (opens in a new tab)

Explanation

Inferentia instances provide lower-cost inference for models that compile for them, and SageMaker Neo compiles a validated model for that hardware in one step. A larger GPU, a rule-based replacement or turning off monitoring each raise cost, change the service or weaken operations.

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