The answer is to use Amazon SageMaker to deploy a custom LLM. This approach is most suitable because SageMaker gives you full control over the underlying infrastructure, allowing you to optimize for low latency by selecting specific instance types and configuring autoscaling, while also managing costs by only paying for the compute you use during inference. On the AWS Certified AI Practitioner AIF-C01 exam, this question tests your understanding of the trade-offs between managed services and self-managed deployment—specifically, that Amazon Bedrock is a fully managed API service that runs on AWS infrastructure, not your own, making it a common distractor. A key trap is confusing Bedrock’s ease of use with the requirement for custom infrastructure control. Memory tip: Think “SageMaker = Self-managed” for scenarios demanding low latency and cost control over a custom model.
AIF-C01 Fundamentals of Generative AI Practice Question
This AIF-C01 practice question tests your understanding of fundamentals of generative ai. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company wants to use a large language model to generate code based on natural language descriptions. They need to minimize latency and control costs by running inference on their own infrastructure. Which approach is most suitable?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "minimum / minimize"
Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use Amazon SageMaker to deploy a custom LLM
Option B, using Amazon SageMaker to deploy a custom LLM, allows the company to run inference on their own infrastructure with controlled costs and latency. Option A (Amazon Bedrock) is a managed service and does not run on customer infrastructure. Option C (Amazon Lex) is for conversational bots, not code generation. Option D (Amazon Comprehend) is for NLP tasks like sentiment analysis, not code generation.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Use Amazon Bedrock API
Why it's wrong here
Bedrock is a managed service and runs on AWS infrastructure, not the customer's own.
✓
Use Amazon SageMaker to deploy a custom LLM
Why this is correct
SageMaker can deploy models on customer-specified instances, giving control over latency and cost.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Static NAT maps one inside address to one outside address.
✗
Use Amazon Comprehend
Why it's wrong here
Comprehend is for natural language understanding tasks, not generative code output.
✗
Use Amazon Lex
Why it's wrong here
Lex is designed for building conversational interfaces, not for general code generation.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Trap categories for this question
Command / output trap
Comprehend is for natural language understanding tasks, not generative code output.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
→Identify inside and outside interfaces first.
→Check whether the scenario needs static NAT, dynamic NAT or PAT.
→Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AIF-C01 NAT questions on configuration and troubleshooting.
Fundamentals of Generative AI — This question tests Fundamentals of Generative AI — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Use Amazon SageMaker to deploy a custom LLM — Option B, using Amazon SageMaker to deploy a custom LLM, allows the company to run inference on their own infrastructure with controlled costs and latency. Option A (Amazon Bedrock) is a managed service and does not run on customer infrastructure. Option C (Amazon Lex) is for conversational bots, not code generation. Option D (Amazon Comprehend) is for NLP tasks like sentiment analysis, not code generation.
What should I do if I get this AIF-C01 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AIF-C01 NAT questions on configuration and troubleshooting.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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