Domain 5 – Model Selection and Optimisation (16.8%)

4 lessons, each ending with a practice scenario (Pro)

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1. ✓5.1 — LLM FundamentalsClaude sits on an autoregressive language model: it writes text one token at a time. Three consequences: Generation is serial — response time scales with output tokens; input length affects time-to-first-token; No lookahead, no revision — once a token is emitted it is part of the record; state the output contract up front; Early wrong turns compound — each token conditions the next.Scenario: Pro
2. ✓5.2 — Technical FundamentalsA Claude application talks to the API over HTTP with JSON. Official SDKs wrap the wire in seven languages (Python, TypeScript, C, Go, Java, PHP, Ruby) plus the ant CLI.Scenario: Pro
3. ✓5.3 — Model Selection and Trade-offsHaiku → Sonnet → Opus → Frontier. Quality, latency and cost are not independent. Moving up buys capability and spends money and time. Haiku, Sonnet, Opus, Frontier.Scenario: Pro
4. ✓5.4 — Cost and Token ManagementEvery response carries a usage object: inputtokens, cachecreationinputtokens, cachereadinputtokens, outputtokens. Output is priced several times input. Identify which axis dominates before choosing a lever.Scenario: Pro