Get Your CertAIF-C01PlayCompete
‹ All AIF-C01 questions

AIF-C01 Practice Question 64: Serverless Model Access

AWS Certified AI Practitioner (AIF-C01) · Test 7 · question 4 of 10

Fundamentals of GenAImedium

A product team wants to start calling a hosted foundation model this week. It has no ML platform engineers, wants to pay only for what it uses with no infrastructure to size or keep running, and wants to try models from several providers behind one API before committing. A colleague suggests deploying a model from Amazon SageMaker JumpStart. What should the team do, and why?

Select one answer
Show answer and explanation

Answer:

  • C. Use Amazon Bedrock, because it serves models from several providers through one serverless API with no endpoint to provision

Amazon Bedrock is the serverless path to foundation models: one API, several providers, pay per token, and nothing to provision or keep warm. That maps directly onto every constraint in the stem, no platform engineers, no infrastructure sizing, and easy comparison across providers before committing (option C).

- A is wrong on the facts. SageMaker JumpStart genuinely *does* offer pre-trained foundation models; AWS describes it as onboarding and maintaining publicly available foundation models for you to access, customise and integrate. It is not the only route, and the reason to prefer Bedrock here is the operating model, not the catalogue. - B is wrong because a JumpStart model is deployed to SageMaker instances that you choose and pay for by the hour for as long as the endpoint exists, whether or not requests arrive. Dedicated capacity can beat per-token pricing at sustained heavy volume, but "at any volume" is false, and it is the wrong shape for a team starting this week. - D is wrong because pre-training a model is the slowest and most expensive option available, needs skills the team does not have, and answers none of the stated requirements.

Reference: Amazon SageMaker JumpStart Foundation Models

Practice Test 7 ›

Study guides for AIF-C01

AI engineer roadmap 2026: skills, certs and the orderAI engineer roadmap for 2026: skills to learn in order, AI engineer vs ML engineer, which certifications fit each stage, and what to skip.
How AI Is Changing Software Jobs: Where to PrepareHow AI is changing software jobs: what history shows, the evidence on junior developers, what Klarna learned, new AI roles and five domains to prepare for.
How AWS Certification Exams Work: Scoring and RenewalHow AWS certification exams work: levels, booking, question types, unscored questions, scaled scores, pass marks, validity and how recertification works.
What Is Amazon Bedrock? Models, Pricing and GuardrailsWhat is Amazon Bedrock? How a request reaches a model, what happens to your data, token pricing, Guardrails, Knowledge Bases, AgentCore and the exam traps.
Google Cloud Generative AI Leader vs AWS AI PractitionerGoogle Cloud Generative AI Leader vs AWS AI Practitioner: format, cost, topics, free preparation and which certification to take first.
AWS certification cost in 2026, and free ways to prepareWhat every AWS certification costs in 2026 in USD and INR, retake and reschedule rules, the 50% voucher, and the free official ways to prepare.
AWS Generative AI Developer Professional (AIP-C01) guideWhat the AWS Certified Generative AI Developer – Professional (AIP-C01) exam covers - format, cost, the five domains, the services, and how to prepare.
Best AI certifications 2026: AWS, Google, Microsoft, ClaudeThe best AI certifications in 2026 from AWS, Google, Microsoft, Claude and Databricks, by level, with price, status and which one to take first.
Which AWS certification should you get? 2026 roadmapWhich AWS certification to get first, by role and experience, with each exam's status in October 2026, what is changing, and prices in USD and INR.
What Is RAG? How Retrieval-Augmented Generation WorksWhat RAG is and how it works: chunking, embeddings, vector search, hybrid search, reranking, GraphRAG and agentic RAG on AWS, Google and Azure.
Prompt engineering vs RAG vs fine-tuningWhen to use prompt engineering, retrieval-augmented generation, fine-tuning or continued pre-training, compared on cost, freshness, hallucination and privacy.