Beginner's guide to AI practitioner

Exam Blueprint & Structure

• Format: 65 questions (including multiple choice, multi-select, ordering, and matching) completed within 90 minutes.
• Scoring: Uses a scaled score ranging from 100 to 1,000, with a minimum passing score of 700.

Core Exam Domains

• AI/ML & GenAI Fundamentals (44%): Covers traditional ML types, evaluation metrics, large language models (LLMs), tokens, embeddings, and context windows.
• Foundation Model Applications (28%): Focuses on choosing between Amazon Bedrock and Amazon SageMaker, Retrieval-Augmented Generation (RAG), prompt engineering, and multi-modal workflows.
• Responsible AI & Governance (28%): Explores bias mitigation, data privacy, safety guardrails, IAM access control, and the AWS Shared Responsibility Model.

Recommended Study Path on AWS Skill Builder

• Digital Courses: Enroll in the official AWS AI Practitioner Learning Plan on AWS Skill Builder for structured video modules covering all domains.
• Practice Exams: Utilize official diagnostic sets and full-length practice tests on AWS Skill Builder to benchmark your readiness and test pacing.

Essential Strategy Tips

• Prioritize Concepts Over Code: You will not need to write Python scripts or calculate neural network weights. Focus on business scenarios, high-level architecture, and service trade-offs.
• Master Service Boundaries: Clearly distinguish when to choose managed APIs like Amazon Bedrock over customized model building platforms like Amazon SageMaker.