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MLA-C02 practice question: Deploying foundation models (2 of 2)

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.

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A regional bank runs a customer-service assistant on a Bedrock foundation model. Traffic is steady and high during business hours in every time zone the bank serves, and the bank needs predictable latency and throughput for its peak periods.

The bank wants a commitment that guarantees capacity for its model usage, and it accepts paying for that reserved capacity for a fixed term.

Which Bedrock option should the bank choose?

Choose one answer.

  1. A. On-demand inference, which charges per token and lets the bank send as many requests as the service can accept at the time.
  2. B. Cross-Region inference, which routes requests to other Regions when the home Region is busy, so that the bank never sees throttling.
  3. C. Provisioned throughput for the model with a commitment term, so that the bank has reserved model capacity for its steady peak load.
  4. D. Batch inference, which processes the requests in a file overnight so that daytime traffic does not reach the model.
Show the answer and explanation

Answer: C

  • A. Wrong: On-demand has no capacity commitment, so throughput can vary during peaks. The stem asks for a guaranteed capacity commitment.
  • B. Wrong: Cross-Region inference helps absorb bursts across Regions, but it does not reserve capacity for a commitment term. The stem asks for guaranteed throughput.
  • C. Right: Correct. Provisioned throughput reserves model capacity for a commitment term, which gives predictable throughput and latency for steady, high traffic. The bank pays for the reserved capacity. Learn more: Increase model invocation capacity with Provisioned Throughput (opens in a new tab)
  • D. Wrong: Batch inference is for offline work and does not serve the bank's interactive assistant. The option removes the requests the stem is about.

Explanation

Provisioned throughput reserves model capacity under a commitment, which suits steady, high traffic that needs predictable throughput. On-demand has no reservation, and cross-Region inference and batch inference do not reserve capacity for interactive peaks.

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