Use this quick start guide to collect all the information about IBM watsonx Generative AI Engineer Associate (C1000-185) Certification exam. This study guide provides a list of objectives and resources that will help you prepare for items on the C1000-185 IBM watsonx Generative AI Engineer - Associate exam. The Sample Questions will help you identify the type and difficulty level of the questions and the Practice Exams will make you familiar with the format and environment of an exam. You should refer this guide carefully before attempting your actual IBM watsonx Generative AI Engineer Associate certification exam.
The IBM watsonx Generative AI Engineer Associate certification is mainly targeted to those candidates who want to build their career in Data, Analytics, and AI domain. The IBM Certified watsonx Generative AI Engineer - Associate exam verifies that the candidate possesses the fundamental knowledge and proven skills in the area of IBM watsonx Generative AI Engineer Associate.
IBM watsonx Generative AI Engineer Associate Exam Summary:
| Exam Name | IBM Certified watsonx Generative AI Engineer - Associate |
| Exam Code | C1000-185 |
| Exam Price | $200 (USD) |
| Duration | 90 mins |
| Number of Questions | 62 |
| Passing Score | 71% |
| Books / Training | IBM Certified watsonx Generative AI Engineer v1.1 - Associate |
| Schedule Exam | Pearson VUE |
| Sample Questions | IBM watsonx Generative AI Engineer Associate Sample Questions |
| Practice Exam | IBM C1000-185 Certification Practice Exam |
IBM C1000-185 Exam Syllabus Topics:
| Topic | Details | Weights |
|---|---|---|
| Analyze and Design a Generative AI Solution |
- Understand the five capabilities of GenAI/LLMs - Articulate the components in Gen AI Patterns - Understand the limitations of GenAI/LLMs - Understand use cases and identify Gen AI application opportunities - Understand how to choose the appropriate model for a use case - Articulate the optimal model architecture based on a 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 |
15% |
| Prompt Engineering |
- 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 - Articulate hyper parameter tuning - Articulate model risks |
16% |
| Fine-tuning |
- 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 |
31% |
| Retrieval-Augmented Generation (RAG) |
- Describe embeddings in the context of GenAI - Generate vector embeddings utilizing models - Describe when to use a vector database - Develop using libraries |
17% |
| Deployment |
- Plan for a deployment based on client needs - Deploy AI Assets - Deploy a custom model - Plan out deployment of prompts for versioning - High level architecture for deployment options |
13% |
| Integration with Model Orchestration |
- Integrate watsonx.ai with Other Services/Manage APIs and SDKs - Orchestrate AI Workflows - Understand real-world Integration Scenarios - Develop LLM based applications with LangChain |
8% |
To ensure success in IBM watsonx Generative AI Engineer Associate certification exam, we recommend authorized training course, practice test and hands-on experience to prepare for IBM watsonx Generative AI Engineer - Associate (C1000-185) exam.