Your cart is empty.
Keep shopping
C1000-185: IBM watsonx Gen AI Engineer - Associate Prep
New
1 students
C1000-185: IBM watsonx Gen AI Engineer - Associate Prep
Exam-focused preparation for prompt engineering, tuning, RAG & deployment on IBM watsonx
Created byAseem Mankotia
Last updated 9/2026
English
What you'll learn
- Assess business requirements and select the right foundation model for a use case
- Design an end-to-end generative AI solution architecture on IBM watsonx
- Write, test, and refine prompts using recognized prompt engineering patterns
- Use Prompt Lab parameters to control model output quality and behavior
- Decide when prompt tuning vs. parameter-efficient fine-tuning is appropriate
- Prepare training data and evaluate a tuned foundation model
- Build a RAG pipeline with embeddings, a vector store, and grounded retrieval
- Evaluate RAG output quality and reduce hallucination risk
Explore related topics
Course content
13 sections • 12 lectures
Requirements
- Basic Python programming ability
- General familiarity with AI/ML and generative AI concepts
- Comfort with REST APIs and command-line tools
- No prior hands-on watsonx experience required
- A free IBM Cloud account is helpful but not mandatory for this course
Description
This course contains the use of artificial intelligence.
This is an applied, exam-focused preparation course for IBM's C1000-185 watsonx Generative AI Engineer – Associate certification, built for AI/ML engineers, application developers, and data professionals who want to prove hands-on skill with the IBM watsonx stack. Across twelve structured chapters you will work through the six official exam sections in order: analyzing and designing a generative AI solution, prompt engineering with Prompt Lab, prompt and parameter-efficient tuning, retrieval-augmented generation (embeddings, vector stores, grounding), deployment and inference lifecycle, and watsonx integration and model orchestration including agentic, multi-step patterns. Every chapter pairs precise, current watsonx terminology with a concrete scenario, a hands-on lab you can complete without a paid watsonx account, exam-style practice questions, and explicit call-outs of common exam traps.
Your instructor, Aseem Mankotia, structures the material the way the exam is organized rather than the way a textbook is organized, so what you study maps directly onto what you will be asked. The course closes with a full-length exam simulation chapter and a time-management plan for the 90-minute, 62-question format so you walk into the Pearson VUE testing center with a rehearsed strategy, not just memorized facts. Because IBM updates its official exam guide, objective weighting, question count, scoring threshold, and fees periodically, you are encouraged to confirm all current exam details on IBM's official certification page before registering — this course is designed to build the deep, applied watsonx knowledge that outlasts any single version of the exam blueprint.
AI content disclosure: This course was produced with the assistance of artificial intelligence tools. Lecture narration is AI-voice generated, and lecture scripts, slides, and practice questions were drafted with AI assistance, then reviewed and curated by the instructor for technical accuracy and alignment with the official C1000-185 exam guide.
Who this course is for:
- AI/ML engineers, application developers, and data professionals building generative-AI solutions on IBM watsonx - practitioners who need to prove hands-on skills with prompt engineering, fine-tuning, RAG, and deployment. Associate-level; assumes basic Python and general AI familiarity but not deep prior watsonx experience.