AIF-C01 Sample Questions & Answers
Designing with foundation models and prompt engineering carries the biggest share, ahead of generative AI concepts and the AWS infrastructure behind them, core AI and ML fundamentals, and equal portions on responsible AI and security, compliance and governance.
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- Question 1Intermediate
A company is using a pre-trained large language model (LLM) to build a chatbot for product recommendations. The company needs the LLM outputs to be short and written in a specific language.Which solution will align the LLM response quality with the company's expectations?
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Correct answer: A
- Question 2Intermediate
A company uses Amazon SageMaker for its ML pipeline in a production environment. The company has large input data sizes up to 1 GB and processing times up to 1 hour. The company needs near real-time latency.Which SageMaker inference option meets these requirements?
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Correct answer: C
- Question 3Intermediate
A company is using domain-specific models. The company wants to avoid creating new models from the beginning. The company instead wants to adapt pre-trained models to create models for new, related tasks.Which ML strategy meets these requirements?
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Correct answer: B
- Question 4Intermediate
A company is building a solution to generate images for protective eyewear. The solution must have high accuracy and must minimize the risk of incorrect annotations.Which solution will meet these requirements?
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Correct answer: A
- Question 5Intermediate
A company wants to create a chatbot by using a foundation model (FM) on Amazon Bedrock. The FM needs to access encrypted data that is stored in an Amazon S3 bucket. The data is encrypted with Amazon S3 managed keys (SSE-S3).The FM encounters a failure when attempting to access the S3 bucket data.Which solution will meet these requirements?
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Correct answer: A
- Question 6Intermediate
A company wants to use language models to create an application for inference on edge devices. The inference must have the lowest latency possible.Which solution will meet these requirements?
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Correct answer: A
- Question 7Beginner
Applications of Foundation Models · Understand the effect of inference parameters on model responses
A marketing agency is using Amazon Bedrock to generate creative ad copy for a global campaign. The creative director notices that the generated text is too generic and repetitive. To increase the variety and creativity of the model's output without changing the underlying prompt structure, which inference parameter should the team adjust?
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Correct answer: B
Temperature controls the randomness of the model's output. Increasing the temperature (e.g., closer to 1.0) introduces more randomness, resulting in more creative and varied responses. Lowering it makes the output more deterministic and repetitive.
- Question 8Intermediate
Applications of Foundation Models · Define Retrieval Augmented Generation (RAG)
A financial services company is building a customer service chatbot using a Large Language Model (LLM) on Amazon Bedrock. The company requires that the chatbot strictly answers questions based only on the provided internal policy documents and must not use outside knowledge or hallucinate facts. Which architectural pattern should the developers implement to meet this requirement?
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Correct answer: B
Retrieval Augmented Generation (RAG) allows the model to retrieve relevant information from a trusted knowledge base (the internal policy documents) and generate an answer based solely on that context. This significantly reduces hallucinations and ensures answers are grounded in company data.
flowchart LR User[User Question] --> Ret[Retriever] KB[(Knowledge Base)] --> Ret Ret --> Context[Context + Prompt] Context --> LLM[LLM] LLM --> Answer[Grounded Answer]
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