AIF-C01 Applications of Foundation Models Practice Question
A company needs to summarize thousands of customer reviews daily using a foundation model. The solution must minimize latency and cost while handling variable traffic. Which AWS service should they use?
⚠ Common exam trap
A common mix-up: candidates confuse Amazon Comprehend's pre-built NLP capabilities with foundation model summarization, failing to recognize that Comprehend cannot perform generative abstractive summarization and is limited to extractive tasks like key phrases and sentiment.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Amazon Bedrock with on-demand mode
Amazon Bedrock with on-demand mode is correct because it provides serverless access to foundation models (FMs) with pay-per-use pricing, which minimizes cost for variable traffic and eliminates the need to provision infrastructure. The on-demand mode handles thousands of daily summarization requests with low latency by leveraging AWS's scalable inference infrastructure, making it ideal for variable workloads without upfront commitments.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Amazon SageMaker real-time endpoint
Why it's wrong here
SageMaker real-time endpoints hold continuously running instances, billing for idle capacity regardless of traffic, which contradicts minimising cost under variable load. They suit low-latency synchronous inference on persistent demand. The scenario's daily batch summarisation needs a serverless foundation-model invocation service that scales to zero.
- ✗
Amazon Comprehend
Why it's wrong here
Amazon Comprehend is a pre-trained NLP service for entity, sentiment and key-phrase extraction, not generative summarisation of long review text. A foundation model on Amazon Bedrock performs the summarisation; Comprehend suits classification and insight extraction over existing text.
- ✗
Amazon Lex
Why it's wrong here
Amazon Lex builds conversational voice and text interfaces, not batch summarisation of reviews. Its runtime is tuned for interactive dialogue latency, so processing thousands of documents daily would require custom orchestration and incur unnecessary cost. It would be correct for adding a chatbot front end to an application.
- ✓
Amazon Bedrock with on-demand mode
Why this is correct
Bedrock on-demand mode bills per token processed and automatically scales with variable traffic, avoiding provisioned throughput charges during idle periods. This satisfies both the latency and cost constraints for summarising thousands of reviews daily without capacity planning.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AIF-C01 exam.