A company is building a document Q&A system for a large corpus of legal documents. They need to ensure that the answers are based on the most recent versions of the documents. They also need to minimize the cost of updating the system when documents change. Which approach is most appropriate?

Fine-tune the model on the entire corpus each time a document changes.

Pre-train a custom model on the legal corpus.

Use a large context window to include all documents in the prompt.

Use RAG with a vector database that is updated when documents change.

A financial services company needs to build a document analysis application that extracts information from complex financial reports. They have a large corpus of labeled data and need high accuracy on domain-specific terminology. They also need to keep inference costs low. Which approach best balances accuracy and cost?

Fine-tune a smaller foundation model on the labeled data to specialize it for the domain.

Pre-train a custom model from scratch on financial documents.

Use a large foundation model with extensive few-shot prompting.

Use RAG to retrieve relevant financial documents and include them in the prompt.

A developer must choose a foundation model for a customer service chatbot that must respond in under 2 seconds and support multiple languages. Which selection criterion is most directly relevant to this requirement?

Latency and multilingual support

A company is building an application that generates product descriptions from images. They need to support a high volume of requests and want to minimize cost. They have a large dataset of product images and descriptions. Which approach is most cost-effective?

Fine-tune a large foundation model on the dataset to improve accuracy.

Use a separate image recognition model and a text generation model.

Use a pre-trained multimodal foundation model with prompt caching to reduce costs for repeated prompts.

Pre-train a custom model from scratch on the dataset.

A marketing team is using a foundation model to generate ad copy. They notice that the outputs are too repetitive and lack variety. They want more creative and diverse suggestions while keeping the copy relevant. Which inference parameter adjustment is most appropriate?

Increase the max output length to allow longer responses.

Decrease the temperature to 0.1 to make outputs more focused.

Increase the temperature to a higher value, such as 0.9.

Reduce the input length to give the model less context.

AWSCertified AI Practitioner

Domain 3Objective 1

Task Statement 3.1: Describe Design Considerations for Applications That Use Foundation Models (FMs). AIF-C01 Practice Questions (Page 3)

Part of the Content Domain 3: Applications of Foundation Models domain, which makes up ~32% of our current practice bank. AWS does not publish an official question count, but from its 90-minute exam (~35–60 total, ~11–19 in this domain), expect 3–5 from this objective — we provide 29 practice questions to prepare you well beyond it. (estimate)

29questions here

6free pages

6concepts

Questions 11–15

11expert · hard
A company is building a document Q&A system for a large corpus of legal documents. They need to ensure that the answers are based on the most recent versions of the documents. They also need to minimize the cost of updating the system when documents change. Which approach is most appropriate?
Select an answer first
12expert · hard
A financial services company needs to build a document analysis application that extracts information from complex financial reports. They have a large corpus of labeled data and need high accuracy on domain-specific terminology. They also need to keep inference costs low. Which approach best balances accuracy and cost?
Select an answer first
13foundation · easy
A developer must choose a foundation model for a customer service chatbot that must respond in under 2 seconds and support multiple languages. Which selection criterion is most directly relevant to this requirement?
Select an answer first
14application · medium
A company is building an application that generates product descriptions from images. They need to support a high volume of requests and want to minimize cost. They have a large dataset of product images and descriptions. Which approach is most cost-effective?
Select an answer first
15application · medium
A marketing team is using a foundation model to generate ad copy. They notice that the outputs are too repetitive and lack variety. They want more creative and diverse suggestions while keeping the copy relevant. Which inference parameter adjustment is most appropriate?
Select an answer first

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