Foundation model design considerations, prompt engineering, training/fine-tuning, and evaluation.
This domain covers 28% of the exam and is the largest domain. It evaluates knowledge of pre-trained model selection criteria, inference parameters (temperature, input/output length), Retrieval Augmented Generation (RAG) and Amazon Bedrock knowledge bases, vector databases (OpenSearch, Aurora, Neptune), cost tradeoffs of model customization approaches (pre-training, fine-tuning, in-context learning, RAG), agents in multi-step tasks (Agents for Amazon Bedrock), prompt engineering techniques (chain-of-thought, zero-shot, few-shot), prompt risks (poisoning, hijacking, jailbreaking), fine-tuning methods (instruction tuning, transfer learning, RLHF), and foundation model evaluation metrics (ROUGE, BLEU, BERTScore).
5 minutes
5 Questions
Domain 3: Applications of Foundation Models is a critical component of the AWS Certified AI Practitioner (AIF-C01) exam, focusing on how foundation models (FMs) are practically applied to solve real-world problems. This domain typically accounts for approximately 28% of the exam content.
**Key Areas Covered:**
1. **Design Considerations for FM Applications:** This involves understanding how to select appropriate foundation models based on specific use cases, considering factors like model size, latency requirements, cost, accuracy, and task complexity. Candidates must know when to use pre-trained models versus fine-tuned models.
2. **Prompt Engineering:** A fundamental skill involving crafting effective prompts to elicit desired outputs from FMs. This includes techniques like zero-shot prompting, few-shot prompting, chain-of-thought reasoning, and system prompts. Understanding how prompt design impacts model performance is essential.
3. **Retrieval Augmented Generation (RAG):** This architecture enhances FM responses by retrieving relevant information from external knowledge bases before generating outputs. Candidates should understand vector databases, embedding models, and how RAG reduces hallucinations while keeping responses current and contextually accurate.
4. **Fine-Tuning and Customization:** Understanding methods to adapt foundation models to specific domains, including fine-tuning with domain-specific data, instruction tuning, and the trade-offs between customization approaches.
5. **Agent-Based Solutions:** Knowledge of how FMs can be integrated with tools, APIs, and orchestration frameworks to create autonomous agents that perform multi-step tasks.
6. **AWS Services Integration:** Practical knowledge of services like Amazon Bedrock, Amazon SageMaker JumpStart, Amazon Q, and related services that enable FM deployment, customization, and management.
7. **Model Evaluation:** Understanding metrics and methodologies for assessing FM performance, including benchmarks like ROUGE, BLEU, and human evaluation approaches.
This domain emphasizes practical application knowledge, requiring candidates to understand not just theoretical concepts but how to architect solutions using foundation models within the AWS ecosystem effectively and responsibly.Domain 3: Applications of Foundation Models is a critical component of the AWS Certified AI Practitioner (AIF-C01) exam, focusing on how foundation models (FMs) are practically applied to solve real-world problems. This domain typically accounts for approximately 28% of the exam content.
**Key Are…
