AI Skill Certs
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.

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

Cost and Token Management (2.8%): Token budgeting and cost management techniques for Claude applications, including token usage tracking, cost modeling, and caching techniques (prompt caching, cache check-pointing) for cost optimization.

Other exam domains

Domain 1 · 14.7%Agents and WorkflowsDomain 2 · 33.1%Applications and IntegrationDomain 3 · 3.1%Claude CodeDomain 4 · 2.6%Eval, Testing, and DebuggingDomain 6 · 11%Prompt and Context EngineeringDomain 7 · 8.1%Security and SafetyDomain 8 · 10.6%Tools and MCPs
Back to all concepts