# Content Domain 3: Applications of Foundation Models
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Domain 3 covers applications of foundation models and represents 28% of the scored content on the exam.

**Topics**
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## Task Statement 3.1: Describe design considerations for applications that use foundation models (FMs).
](#ai-practitioner-01-task3.1)
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## Task Statement 3.2: Choose effective prompt engineering techniques.
](#ai-practitioner-01-task3.2)
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## Task Statement 3.3: Describe the training and fine-tuning process for FMs.
](#ai-practitioner-01-task3.3)
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## Task Statement 3.4: Describe methods to evaluate FM performance.
](#ai-practitioner-01-task3.4)

## Task Statement 3.1: Describe design considerations for applications that use foundation models (FMs).
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Objectives:
+ Identify selection criteria to choose pre-trained models (for example, cost, modality, latency, multi-lingual, model size, model complexity, customization, input/output length, prompt caching).
+ Describe the effect of inference parameters on model responses (for example, temperature, input/output length).
+ Define Retrieval Augmented Generation (RAG) and describe its business applications (for example, Amazon Bedrock Knowledge Bases).
+ Identify AWS services that help store embeddings within vector databases (for example, Amazon OpenSearch Service, Amazon Aurora, Amazon Neptune, Amazon RDS for PostgreSQL).
+ Explain the cost tradeoffs of various approaches to FM customization (for example, pre-training, fine-tuning, in-context learning, RAG).
+ Describe the role of agents in multi-step tasks (for example, Amazon Bedrock Agents, agentic AI, model context protocol).

## Task Statement 3.2: Choose effective prompt engineering techniques.
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Objectives:
+ Define the concepts and constructs of prompt engineering (for example, context, instruction, negative prompts, model latent space, prompt routing).
+ Define techniques for prompt engineering (for example, chain-of-thought, zero-shot, single-shot, few-shot, prompt templates).
+ Identify and describe the benefits and best practices for prompt engineering (for example, response quality improvement, experimentation, guardrails, discovery, specificity and concision, using multiple comments).
+ Define potential risks and limitations of prompt engineering (for example, exposure, poisoning, hijacking, jailbreaking).

## Task Statement 3.3: Describe the training and fine-tuning process for FMs.
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Objectives:
+ Describe the key elements of training an FM (for example, pre-training, fine-tuning, continuous pre-training, distillation).
+ Define methods for fine-tuning an FM (for example, instruction tuning, adapting models for specific domains, transfer learning, continuous pre-training).
+ Describe how to prepare data to fine-tune an FM (for example, data curation, governance, size, labeling, representativeness, reinforcement learning from human feedback [RLHF]).

## Task Statement 3.4: Describe methods to evaluate FM performance.
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Objectives:
+ Determine approaches to evaluate FM performance (for example, human evaluation, benchmark datasets, Amazon Bedrock Model Evaluation).
+ Identify relevant metrics to assess FM performance (for example, Recall-Oriented Understudy for Gisting Evaluation [ROUGE], Bilingual Evaluation Understudy [BLEU], BERTScore).
+ Determine whether a FM effectively meets business objectives (for example, productivity, user engagement, task engineering).
+ Identify approaches to evaluate the performance of applications built with FMs (for example, RAG, agents, workflows).