OpenExamsFREE PRACTICE — ANY EXAM

IBM watsonx Generative AI Engineer v1 - Associate — Information & Free Practice

US · Examination information

100 free questions · Answer explanations · No signup required

Start free practice →

IBM watsonx Generative AI Engineer v1 - Associate

Provider
Pearson VUE
Format
Proctored examination; the current official record flags multiple choice, build list and hot area items without publishing type shares.
Exam questions
Total questions: 62.
Time limit
90 minutes
Passing score
44 questions to pass
Examination outline
  • Analyze and Design a Generative AI Solution (15%)
    • Understand the 5 Capabilities of GenAI models/LLMs
    • Articulate the Components in Gen AI Patterns
    • Understand the Limitations of GenAI/LLMs
    • Understand Use Case and Identify Gen AI Application Opportunities
    • Understand How to Choose the Appropriate Model for a Use Case
    • Articulate the Optimal Model Architecture Based on Use Case
    • Identify and apply various tools and techniques like AI agents, RAG, LangChain, etc.
    • Understand security risks associated with LLMs, prompt engineering, prompt, and data
  • Prompt Engineering (16%)
    • Differentiate between zero-shot and few-shot prompting
    • Design Prompts based on use case
    • Generate Prompt Templates
    • Determine the best model parameters for each GenAI prompt
    • Describe the benefits of using prompt variables
    • Describe the benefits of Prompt Lab
    • Controlling model parameters and hyper parameter tuning
    • Articulate model risks
  • Fine-tuning (31%)
    • Understand the difference between hard and soft prompts
    • Reconstruct prompts to reduce the cost of using GenAI models
    • Plan for Data elements for application usage
    • Articulate model quantization techniques
    • LoRA
    • Prepare the dataset for training
    • Customize LLMs with InstructLab
    • Generate synthetic data using the User Interface
  • Retrieval-Augmented Generation (RAG) (17%)
    • Describe what embeddings are in Context of GenAI
    • Generate vector embeddings utilizing models
    • Describe when to use a vector database
    • Develop using libraries and tools
  • Deployment (13%)
    • Plan a deployment based on client needs
    • Deploy AI Assets
    • Deploy a custom model
    • High-level architecture for deployment options
    • Plan the deployment of prompts for versioning
  • watsonx - Integration and Model Orchestration (8%)
    • Integrate watsonx.ai with Other Services
    • Orchestrate AI Workflows
    • Understand real-world Integration Scenarios
    • Develop LLM based applications with LangChain
Examination sources

Related license preparation

This examination may be required or accepted as an alternative, depending on the license and application pathway.