IBM C1000-185 Exam Details & Actual Exam Questions
- Exam Code/Number: C1000-185
- Exam Name/Title: IBM watsonx Generative AI Engineer - Associate
- Certification Provider: IBM
- Corresponding Certification: IBM Certified watsonx Generative AI Engineer - Associate
- Exam Questions: 380
- Updated On: Sep,28 2026
- Certification Level: Associate
IBM watsonx Generative AI Engineer - Associate Exam Questions
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(31 Up Votes)IBM C1000-185 Exam Overview:
| Certification Vendor: | IBM |
|---|---|
| Exam Name: | IBM watsonx Generative AI Engineer - Associate |
| Exam Number: | C1000-185 |
| Exam Price: | $200 USD |
| Passing Score: | 62% |
| Available Languages: | English |
| Exam Format: | Multiple select, Multiple choice |
| Exam Duration: | 90 minutes |
| Related Certifications: | IBM Certified watsonx Generative AI Engineer - Associate |
| Real Exam Qty: | 62 |
| Sample Questions: | IBM C1000-185 Sample Questions |
| Exam Way: | Online or Test Center (Pearson VUE) |
| Pre Condition: | None, but hands-on experience with IBM watsonx.ai Studio is highly recommended. |
| Official Syllabus URL: | https://www.ibm.com/training/certification/C1000-185 |
IBM C1000-185 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Analyze and Design a Generative AI Solution | 15% | - Articulate the components in Gen AI Patterns - Understand the limitations of GenAI/LLMs - Understand use cases and identify Gen AI application opportunities - Understand the five capabilities of GenAI/LLMs - Understand how to choose the appropriate model for a use case - Understand security risks associated with LLMs, prompt engineering, prompt, and data - Identify and apply various tools and techniques like AI agents, RAG, LangChain, etc. - Articulate the optimal model architecture based on a use case |
| Deployment & Enterprise Readiness | - Improving solutions based on user feedback - Understanding basic security and access control requirements - Managing usage and monitoring at a basic level - Preparing GenAI solutions for enterprise usage | |
| Retrieval-Augmented Generation (RAG) | 17% | - Describe when to use a vector database - Develop using libraries - Generate vector embeddings utilizing models - Describe embeddings in the context of GenAI |
| Prompt Engineering & Output Quality | 25% | - Improving output quality using prompt design techniques - Reducing hallucinations and improving overall output accuracy - Understanding foundational Prompt Engineering techniques - Writing effective and professional prompts - Controlling response style, length, and format |
| Integration with Model Orchestration | 8% | - Orchestrate AI Workflows - Integrate watsonx.ai with Other Services/Manage APIs and SDKs - Develop LLM based applications with LangChain - Understand real-world Integration Scenarios |
| Deployment | 13% | - Plan for a deployment based on client needs - High level architecture for deployment options - Plan out deployment of prompts for versioning - Deploy a custom model - Deploy AI Assets |
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