MLA-C02 practice question: Real-time, serverless, async and batch inference (2 of 3)
Deployment and Orchestration of ML and AI Workflows 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.
A logistics broker has an internal model that scores carrier invoices for duplicate billing. The tool is used by a few analysts during business hours, and it receives only a handful of requests per day with small payloads.
The broker wants the lowest cost for this light, unpredictable traffic and does not want to manage instance capacity, while still accepting that a request after an idle period may take a little longer.
Which endpoint type should the team choose?
Choose one answer.
AA. A real-time endpoint on a GPU instance that runs at all times, so that every request is answered at the same speed.
BB. A batch transform job that is started by hand each time an analyst wants a score for a new invoice.
CC. A serverless inference endpoint, which charges for the compute used during requests and needs no instance capacity to be managed.
DD. An Amazon EC2 instance running a web server that loads the model in memory and is managed by an auto scaling group with a minimum of two instances.
Show the answer and explanation
Answer: C
A. Wrong: A running GPU instance is paid for all day even though the traffic is a handful of requests. This is the highest cost for the light workload.
B. Wrong: Batch transform works for a set of records processed together, and it is not a request-time service for a single invoice. Starting a job by hand for each invoice adds delay and effort.
D. Wrong: Two always-on instances cost more than the light traffic justifies, and the broker would manage the servers itself. The stem asks for lower cost with no capacity management.
Explanation
Serverless inference suits intermittent traffic with small payloads, because the broker pays for compute used and does not manage instances. A running real-time endpoint or two instances pay for idle time, and batch transform is not built for single on-demand requests.
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