Skills Measured for IBM AI Engineering

You should know how Prompt Lab supports experimentation with foundation models, prompt instructions, variables, and output review. The key skill is not copying a prompt pattern; it is recognizing when prompt wording, model choice, generation settings, or evaluation evidence explains the result.

Prompt engineering includes measuring output quality. Evaluation workflows help compare prompt templates, while generation parameters influence randomness, length, and output behavior. Scenario questions often test whether to refine the prompt, adjust settings, add context, evaluate outputs, or move toward tuning.

AutoAI skills include knowing how automated experiments build and compare pipelines, while still requiring human review of data, metrics, constraints, and deployment readiness. AutoAI can reduce manual model-building work, but it does not remove the need to interpret results or validate the business fit.

Vector indexes support retrieval workflows by making document chunks or embeddings searchable for relevant context. This matters when a generative AI solution needs grounding in approved data instead of relying only on a model's internal knowledge. Study how retrieval affects answer quality, governance, and source control.

Tuning skills include knowing when a model needs examples that shape behavior beyond ordinary prompting. Tuning requires appropriate data formats and evaluation after adaptation. It should not be chosen when a simpler prompt change, generation setting, or retrieval workflow solves the problem.

Deployment skills include understanding deployment spaces, deployable assets, prompt-template deployment, and management of AI assets. The important distinction is prototype versus deployable asset: production use needs controlled release movement, access, version awareness, and operational review.

Governance skills include evaluating AI behavior, documenting assets, handling sensitive data responsibly, and applying ethical AI principles. Questions may hide governance requirements inside technical scenarios, especially when outputs affect users, regulated data, or business decisions.