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IBM Certified watsonx Generative AI Engineer - Associate — Requirements & Free Practice

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IBM Certified watsonx Generative AI Engineer - Associate

Associate-level IBM certification for selecting, customizing and prompting large language models and designing and developing generative AI solutions using watsonx.ai studio.

Licensing authority: IBM

Initial by examination
IBM watsonx Generative AI Engineer v1 - Associate
Agreement
The candidate must accept the IBM Certification Agreement before the credential is issued.
Experience
Associate examinations presume six months to one year of hands-on experience; the page recommends Python, data analysis and data science. This is preserved as a readiness expectation, not converted to a separately awarded prerequisite.
Preparation
The learning path, sample and assessment tests are preparation resources. The Study Guide's Next Steps recommends sample and assessment practice; the certification Requirements section names only C1000-185. IBM permits candidates to decide they are already prepared using prior education, experience or job skills.
Registration
Create an IBM-testing Pearson VUE account and schedule an available appointment. Present the required original valid government ID, with matching registration name; online proctoring additionally requires a successful system check and a private, quiet, clean environment.
Requested Horizon
2026-11-25 through 2027-02-28; current Live status does not reserve an appointment or establish school-arranged availability.

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