watsonx GenAI Engineer study guide
watsonx GenAI Engineer · Work through your exam, one topic at a time.
The watsonx Generative AI Engineer credential is aimed at practitioners who build, tune, govern, and operate generative AI solutions on the IBM watsonx stack. It suits engineers and architects who already write code, call APIs, and reason about data pipelines, and who now need to demonstrate they can take a foundation model from prompt to production responsibly. The watsonx GenAI Engineer study guide has 8 sections and about 2,655 words.
01watsonx platform fundamentals and architecture
This domain tests whether you can map a business requirement onto the right watsonx component and explain how the pieces fit together at runtime. watsonx.ai - build, tune, and serve foundation models and classical ML: Prompt Lab, Tuning Studio, AutoAI, Jupyter notebooks, and a model catalog of IBM Granite plus third-party open models such as Llama, Mistral and flan-ul2.
340 words, about 2 min02Foundation models and prompt engineering
This domain tests how well you choose a model and shape a prompt, and whether you can identify which parameter produced a bad output. Every foundation model has a context window; prompt tokens plus max new tokens must fit inside it.
334 words, about 2 min03Retrieval-augmented generation and vector search
RAG questions test whether you can trace a wrong answer back to ingestion, retrieval, or generation. Ingest → parse → chunk → embed → index → retrieve top-k → optionally rerank → assemble prompt with context → generate with citations.
340 words, about 2 min04Model tuning and customization
Tuning questions test whether you can pick the cheapest intervention that changes behaviour, and prepare data that actually teaches. Prompt engineering - no weights change; try this first.
335 words, about 2 min05AI governance, risk, and compliance with watsonx.governance
Governance questions test whether you can produce evidence and controls across the whole model lifecycle, not just at go-live. Model inventory - every model, owner and risk classification in one register.
327 words, about 2 min06Generative AI application development and orchestration
Application-development questions test whether you can turn a prompt into a reliable service: orchestration, tools, guardrails and failure handling. Prompt templates with typed variables, versioned separately from code.
324 words, about 2 min07Deployment, monitoring, and lifecycle operations
Deployment questions test the path from an asset in a project to a monitored, versioned endpoint - and what you watch once it is live. A tuned model, prompt template or function is promoted from a project into a space and deployed with watsonx.ai Runtime.
323 words, about 2 min08Data preparation and integration for generative AI
Data questions test whether you can turn messy enterprise content into governed, retrieval-ready and training-ready assets. watsonx.data is an open lakehouse: object storage (IBM COS or S3) with open table formats such as Iceberg, Hudi and Delta, queried through Presto and Spark, with governed connections to sources like Db2 and Snowflake.
332 words, about 2 min