easyMultiple Select
AIF-C01 Practice Question: A developer wants to use Amazon Bedrock to build…
A developer wants to use Amazon Bedrock to build a text summarization application. Which TWO of the following are required steps?
⚠ Common exam trap
AWS often tests the misconception that you must train or host your own model to use generative AI, when in fact managed services like Bedrock provide pre-trained models accessible via API, eliminating the need for custom infrastructure.
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
✓
Request access to a foundation model in Amazon Bedrock
Option D is correct because Amazon Bedrock requires you to request and be granted access to a specific foundation model (for example, Amazon Titan Text or Anthropic Claude) in the target AWS Region before you can invoke it, since models are not enabled by default. Option E is correct because once access is granted, the application must call the model through the Bedrock runtime API (InvokeModel or Converse) using the AWS SDK or CLI, passing a prompt that contains the text to be summarized. Option A is incorrect because Bedrock is a fully managed, serverless service that exposes foundation models via API, so no SageMaker endpoint needs to be created or managed. Option B is incorrect because Bedrock provides pre-trained foundation models that can be used as-is or customized with techniques like fine-tuning or RAG, but training a model from scratch is not required. Option C is incorrect because Amazon EMR is not a prerequisite for Bedrock; any text preprocessing can be done in the application itself or with simpler services, and EMR is unnecessary for this scenario.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Create an Amazon SageMaker endpoint for hosting the model
Why it's wrong here
Amazon Bedrock provides serverless access to foundation models through its own API, so no SageMaker endpoint is required. It is tempting because SageMaker endpoints are the correct approach when hosting a custom or self-managed model, but Bedrock abstracts that infrastructure away entirely.
- ✗
Train a custom model from scratch on summarization data
Why it's wrong here
Bedrock provides pre-trained foundation models accessible via API, so training from scratch is unnecessary and would forfeit the managed service's benefit. It is tempting because custom training suits domain-specific accuracy when no suitable foundation model exists, but here it duplicates work Bedrock already performs.
- ✗
Set up an Amazon EMR cluster to preprocess the text data
Why it's wrong here
Bedrock accepts text directly through its API, so an EMR cluster adds needless preprocessing infrastructure. It is tempting because EMR handles large-scale data transformation for custom model training pipelines, which would be the correct choice when preparing massive datasets for a self-managed model.
- ✓
Request access to a foundation model in Amazon Bedrock
Why this is correct
Requesting model access in Amazon Bedrock is mandatory before invocation, since Anthropic, Meta and Amazon models are gated by default. This satisfies the stem's requirement that the developer must obtain entitlement to a specific foundation model in the target AWS Region before the summarisation application can call it.
- ✓
Invoke the model using the Bedrock API or SDK with a prompt containing the text to summarize
Why this is correct
Invoking the model through the Bedrock API or SDK is essential: it is the only mechanism that submits the prompt containing the source text and returns the generated summary. Without this runtime call, no inference occurs, so the summarization requirement in the stem cannot be satisfied.
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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JA
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.