A multi-agent batch analytics job can spawn dozens of short-lived w…

Question

A multi-agent batch analytics job can spawn dozens of short-lived worker agents overnight, then drop to near-zero load by day. Requirements emphasize elastic scale, reliability, operational efficiency, security isolation, and cost control. Which compute specification approach is most appropriate?

Accepted Answer

Specify elastic, container-based or serverless Azure compute that scales with agent concurrency, isolates workloads, and idles cheaply when spawning drops—meeting scalability, reliability, efficiency, security, and cost goals — Correct answer: A. Architects specify compute components against scalability, reliability, operational efficiency, security, and cost. Bursty multi-agent spawning fits elastic container/serverless patterns on Azure—not closet servers (B), laptop ops (C), or static over-provisioning (D). Pair compute choice with concurrency controls and quotas.

Answer

Purchase a large always-on dedicated physical server in an office closet for all agents

Answer

Run production agent workers only on engineers’ laptops when they remember to open a terminal

Answer

Ignore cost and permanently over-provision maximum peak capacity on premium VMs with no autoscale

A multi-agent batch analytics job can spawn dozens of short-lived worker agents overnight, then drop to near-zero load by day. Requirements emphasize elastic scale, reliability, operational efficiency, security isolation, and cost control. Which compute specification approach is most appropriate?

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