Domain 5: Model Selection and Optimization

Self-check: Model Selection and Optimization

Two separate things. The recall prompts come from this domain's notes and you grade yourself on them; the quiz below is scored against original bank items by the same rule the mock uses. Only the quiz feeds a readiness signal.

Recall prompts

Answer from memory, then grade yourself. Nothing here is scored, and none of it counts towards readiness — the quiz below is the half that does.

Technical Fundamentals

  1. Which single endpoint carries tool use, structured output, caching, thinking, and vision? Name the supporting endpoints that feed or describe it.

  2. Name four things an official SDK gives you that a raw HTTP client does not.

  3. Which HTTP status codes does the SDK retry by default, and which does it deliberately not retry?

  4. At roughly what output size does streaming stop being a preference and become a requirement, and what fails if you ignore that?

  5. A workload moves to Amazon Bedrock. What changes besides the credentials?

  6. Why does catching a single broad API exception cause an operational problem rather than merely being untidy?

LLM Fundamentals

  1. What replaced `budget_tokens` on current models, and what happens if you send it anyway?

  2. Which model in the current lineup still uses manual extended thinking?

  3. Where exactly is the effort parameter set? Name the field path.

  4. Which effort level is the default, and what is the consequence of setting it explicitly?

  5. Why does changing effort partway through a conversation cost money beyond the effort change itself?

  6. You index into `content[0]` expecting a thinking block and it is a text block. What did you assume that adaptive thinking does not guarantee?

  7. How do you reduce output variability now that `temperature` is removed?

  8. State two ways fast mode differs from raising effort.

  9. A prompt of fixed length is moved from a pre-4.7 model to a current one. What happens to the token count, and what should you do about it?

Cost and Token Management

  1. What are the two structural discounts, and what does each cost relative to the base price?

  2. In what order are `tools`, `system`, and `messages` rendered for caching, and why does the order matter?

  3. How many cache breakpoints can one request carry?

  4. `usage.cache_read_input_tokens` is zero across repeated requests with what you believe is an identical prefix. Name three likely causes.

  5. Thinking tokens are billed as which kind of token, and which limit do they count against?

  6. A response stops with `stop_reason: "max_tokens"`. Name the two remedies and the question that decides between them.

  7. Does `display: "omitted"` reduce what you are billed? Explain.

  8. Which usage field tells you how much of the billed output was reasoning?

  9. Why is cost per completed task the right unit rather than cost per request?

Model Selection and Tradeoffs

  1. Name the four current models in capability order, with their per-MTok input and output prices.

  2. Claude Haiku 4.5 differs from the rest of the lineup on three axes at once. Name all three.

  3. Which models have a 1M-token context window, and which does not?

  4. What is the maximum synchronous output for the 1M-context models?

  5. Is `claude-opus-5` an alias or a pinned snapshot? What is the practical consequence?

  6. List the four breaking changes that make a model migration more than a string swap.

  7. Your evals fall short on Claude Opus 5 at high effort. What are the two next moves, and in which order would you try them?

  8. Why does a two-model cost cascade forfeit something a single model at lower effort keeps?

  9. What does a published retirement commitment let you do that an unannounced removal would not?

Cross-cutting

  1. This domain is 16.8% of the exam, roughly 9 items. Which of its four sub-skills carries the most weight, and does your study time reflect that?

  2. For each claim you just recited, could you point to the row in `SOURCES.md` that backs it? An unsourced claim you are confident about is the most dangerous kind.

Scored quiz

This quiz asks 9 items — Model Selection and Optimization's share of a 53-item mock. Domains carry different weights, so quizzes are different lengths.

Scored by the same rule as the mock: an item is right only when your selection is exactly the correct set. This is the only part of this page that feeds a readiness signal.

Type to search notes, guides, study plans, cheatsheets, and the blueprint.