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MLA-C02 practice question: Choosing foundation models in Bedrock

ML Model and Foundation Model (FM) Development 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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An esports league runs a fan chatbot that answers short questions about schedules, rules and team rosters. It expects about 200,000 questions a day, and most answers must start appearing within one second.

Answer quality must stay high for these simple questions, and the monthly model cost must stay low. The team plans to choose a foundation model in Amazon Bedrock and test it on its own question set.

Which approach should the team take to choose the model?

Choose one answer.

  1. A. Shortlist smaller Bedrock models, run each on the team's labeled question set, and pick the cheapest one that meets the quality bar at the latency target.
  2. B. Choose the largest foundation model available in Bedrock, since the biggest model gives the best answers for every question.
  3. C. Train a custom language model from scratch in SageMaker AI on the league's past chat logs, and test foundation models only afterward.
  4. D. Use the model with the largest context window, because a larger window always reduces latency for short questions.
Show the answer and explanation

Answer: A

  • A. Right: Correct. Evaluating candidate models on the team's own questions measures quality for the real workload, and comparing latency and cost narrows the choice. This matches how Bedrock model choice should be made. Learn more: Evaluate the performance of Amazon Bedrock resources (opens in a new tab)
  • B. Wrong: The largest model usually responds more slowly and costs more per request, which conflicts with the one-second and cost goals. Size alone does not show quality on these simple questions.
  • C. Wrong: Training from scratch is costly and slow, and it is unnecessary for simple questions that existing models already answer well. The stem asks for a fast, cheap choice from available models.
  • D. Wrong: A large context window does not make short questions faster, and the window size is unrelated to answer quality on this task. Context size should match the amount of text the app sends.

Explanation

Model choice for a Bedrock workload should weigh quality on the team's own data against latency and cost. The largest model or a from-scratch model adds cost and delay, and context window size does not speed up short answers.

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