Domain 5·16.8% of exam·12 concepts
Model Selection and Optimization
Reason about LLM fundamentals, pick the right Claude model for the task, and manage tokens and cost with budgeting and caching.
Official prep for this domain
Anthropic's own free prep module for this part of the syllabus, on the official prep course. Free with an Anthropic Academy sign-in.
5.1
LLM Fundamentals (5.2%): Basic understanding of LLMs (tokens, context windows, sampling, non-determinism, next-token generation), model options (fast mode, extended thinking, adaptive thinking, effort levels), and fundamental prompting techniques (zero-shot, single-shot, multi-shot).
5.2
Technical Fundamentals (6.1%): Foundational technical concepts supporting AI application development, including basic engineering practices (integrating with SDKs that wrap REST APIs, websockets).
5.3
Model Selection and Tradeoffs (2.7%): Claude model capabilities (Opus vs. Sonnet vs. Haiku use cases, adaptive thinking support), tradeoffs across quality/latency/cost parameters, and breaking behavior changes across model releases when selecting models for tasks.
5.4