Amazon Web Services
AWS Certified AI Practitioner
Practitioner-level preparation for AI/ML on AWS, including foundation models, generative AI on Bedrock and SageMaker, prompt engineering, responsible AI, and AI security/governance. Targets candidates with up to ~6 months of AI/ML exposure.
Exam Blueprint
Fundamentals of AI and ML
Basic AI/ML/deep-learning terminology, types of inferencing (batch, real-time), data types (labeled/...
Fundamentals of Generative AI
Core gen AI concepts (tokens, embeddings, prompt engineering, transformer LLMs, foundation models, m...
Applications of Foundation Models
FM design considerations, RAG with Bedrock knowledge bases, vector DB choices (OpenSearch, Aurora, N...
Guidelines for Responsible AI
Bias, fairness, inclusivity, robustness, safety, veracity. Bedrock Guardrails, sustainability, legal...
Security, Compliance, and Governance for AI Solutions
Securing AI systems with IAM, encryption, Macie, PrivateLink, the shared responsibility model. Sourc...
Question Bank
140
Recall
160
Application
0
Analysis
What You'll Study
Fundamentals of AI and ML
- 1.1Explain basic AI concepts and terminologies
- 1.2Identify practical use cases for AI
- 1.3Describe the ML development lifecycle
Fundamentals of Generative AI
- 2.1Explain basic concepts of generative AI
- 2.2Capabilities and limitations of generative AI
- 2.3AWS infrastructure for generative AI
Applications of Foundation Models
- 3.1Design considerations for FM applications
- 3.2Effective prompt engineering techniques
- 3.3Training and fine-tuning process
- 3.4Methods to evaluate FM performance
Guidelines for Responsible AI
- 4.1Develop responsible AI systems
- 4.2Transparent and explainable models
Security, Compliance, and Governance for AI Solutions
- 5.1Methods to secure AI systems
- 5.2Governance and compliance regulations
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