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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Deployment | 13% | - Deploy a custom model - Plan for a deployment based on client needs - Deploy AI Assets - High level architecture for deployment options - Plan out deployment of prompts for versioning |
| Topic 2: Retrieval-Augmented Generation (RAG) | 17% | - Describe when to use a vector database - Describe embeddings in the context of GenAI - Generate vector embeddings utilizing models - Develop using libraries |
| Topic 3: Prompt Engineering & Output Quality | 25% | - Reducing hallucinations and improving overall output accuracy - Improving output quality using prompt design techniques - Understanding foundational Prompt Engineering techniques - Controlling response style, length, and format - Writing effective and professional prompts |
| Topic 4: Deployment & Enterprise Readiness | - Preparing GenAI solutions for enterprise usage - Managing usage and monitoring at a basic level - Improving solutions based on user feedback - Understanding basic security and access control requirements | |
| Topic 5: Analyze and Design a Generative AI Solution | 15% | - Understand the limitations of GenAI/LLMs - Articulate the optimal model architecture based on a use case - Understand use cases and identify Gen AI application opportunities - Understand the five capabilities of GenAI/LLMs - Identify and apply various tools and techniques like AI agents, RAG, LangChain, etc. - Articulate the components in Gen AI Patterns - Understand how to choose the appropriate model for a use case - Understand security risks associated with LLMs, prompt engineering, prompt, and data |
| Topic 6: Integration with Model Orchestration | 8% | - Develop LLM based applications with LangChain - Integrate watsonx.ai with Other Services/Manage APIs and SDKs - Orchestrate AI Workflows - Understand real-world Integration Scenarios |
You're developing a generative AI system for a medical diagnosis application that uses patient data. Your responsibility includes designing prompts that extract valuable insights without exposing sensitive patient information.
Which of the following steps is the most effective way to reduce model risks related to privacy while ensuring useful outputs from the AI?
Correct Answer: B 🗳️
What is one primary advantage of using Prompt Lab in IBM Watsonx when evaluating prompt variations for a generative AI model?
Correct Answer: D 🗳️
A team is fine-tuning a large language model (LLM) for a healthcare application. They have decided to implement a taxonomy tree-based curation to prepare their dataset of medical records and patient interactions.
What is the primary benefit of using a taxonomy tree in the curation process for such a model?
Correct Answer: D 🗳️
You are working with a healthcare provider to deploy a generative AI model that assists in diagnosing patient conditions based on medical history and symptoms. The healthcare provider is concerned about ethical use, bias, and compliance with health data regulations such as HIPAA.
Which governance approach is the most critical to ensure both compliance and ethical AI deployment?
Correct Answer: D 🗳️
In the context of Generative AI (GenAI), various embedding models are used to represent textual data.
Which of the following best describes the difference between Word2Vec, BERT, and Sentence-BERT embedding models?
Correct Answer: A 🗳️
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