A1000-185 IBM watsonx Generative AI Eng. - Assoc. Mock Tests

1. Analyze and Design a Generative AI SolutionThis section establishes the foundational understanding of how to scope and architect a GenAI application.GenAI/LLM Capabilities: Understanding the five core capabilities: Summarization, Transformation (Conversion), Question Answering (Retrieval), Reasoning/Planning (Orchestration), and Content Generation.Architecture Patterns: Identifying components in GenAI patterns, including data ingestion, the serving/deployment layer, and the governance/monitoring layer.Use Case Identification: Mapping specific business problems to appropriate GenAI solutions and identifying opportunities for automation.Model Selection: Criteria for selecting the appropriate foundation model based on performance, cost, latency, and specific use-case requirements (e.g., IBM Granite model family).Tooling and Techniques: Knowledge of AI agents, RAG, and framework integrations like LangChain.Risk and Security: Assessing and mitigating risks associated with LLMs, including data privacy, security vulnerabilities in prompts, and model governance.2. Prompt EngineeringFocuses on the art and science of interacting with foundation models to achieve high-quality, predictable outputs.Prompting Techniques: Differentiating between zero-shot and few-shot prompting; designing effective prompts based on use cases.Prompt Lab Utilization: Leveraging IBM Prompt Lab to experiment with parameters and iterations.Optimization: Determining optimal model parameters (e.g., temperature, top-p, max tokens) and understanding the impact of hyperparameter tuning.Template Design: Creating reusable prompt templates and managing prompt variables to enhance efficiency and sc