This is an AI fundamentals certification designed to validate baseline knowledge: what AI/ML is, how models are evaluated, and how to think about risk and responsibility.
What you should be able to do
- Differentiate AI vs ML vs deep learning and match common model types to use cases.
- Understand the end-to-end lifecycle: problem framing → data → training → evaluation → deployment → monitoring.
- Choose appropriate metrics (precision/recall/F1/AUC vs RMSE/MAE) based on the problem.
- Recognize data pitfalls: label noise, class imbalance, leakage, and overfitting.
- Explain basic GenAI ideas: tokens, context window, hallucinations, and “grounded answers”.
- Apply responsible AI principles: bias/fairness, privacy, security, and governance.
- Recognize OCI service areas relevant to AI workloads (concept-level).
Who this exam is for
- Candidates new to AI/ML who want a vendor-aligned baseline.
- Cloud learners who already have OCI fundamentals and want an AI-focused credential.
- Analysts, PMs, architects, and junior engineers working on AI-enabled products.
Efficient prep strategy
- Use the Syllabus as your checklist.
- Drill topic-by-topic (15–25 questions) and keep a “miss list” mapped to objectives.
- Use the Cheatsheet for final-week review of metrics and responsible AI rules.