Simulado Claude Certified Architect, Foundations CCAR-F

A document-extraction pipeline processes a million documents monthly. A small model handles most documents correctly at low cost but fails on complex ones; the top-tier model handles nearly everything but is roughly an order of magnitude more expensive. Extraction outputs can be checked cheaply (schema validation, arithmetic consistency, confidence signals). What architecture captures most of the quality at a fraction of the cost?

1. Average the small and large models' outputs on every document to blend cost and quality
2. Send documents to a random model weighted 90/10 toward the small one to cap spend probabilistically
3. Run the top-tier model on everything, since extraction errors are unacceptable at any rate
4. An escalation cascade: run the small model first, apply the cheap automated checks to its output, and escalate only failing or low-confidence documents to the top-tier model — so the expensive model runs on the small fraction that actually needs it ✓ (correta)

Every release, an engineer types the same multi-paragraph prompt into Claude Code: gather merged PRs since the last tag, categorize them, and draft release notes in the team's format. The team wants this workflow reusable, parameterized by version, and shared with everyone via the repository. What is the idiomatic Claude Code mechanism?

1. Save the prompt in a personal notes app and paste it each release
2. Ask Claude at session start to memorize the procedure for all future sessions
3. Create a custom slash command (or skill) checked into the repository — e.g., a .claude/commands/release-notes.md file with argument support — so any teammate can invoke the whole workflow with one command ✓ (correta)
4. Hardcode the full procedure into CLAUDE.md so it loads into every session

A data-analysis product lets an agent write and execute Python against user-uploaded datasets. The runtime is currently the API server's own environment. Security review flags that generated code could read environment secrets, reach internal services, or exfiltrate data. What is the required execution architecture?

1. Static-analysis scanning of generated code for dangerous calls before executing it in place
2. A system-prompt instruction forbidding the model from generating code that accesses the network or environment
3. Human review of every generated script before execution, keeping the current runtime
4. Execute generated code only inside an isolated sandbox — an ephemeral, least-privilege container with no credentials or secrets, filesystem access limited to the session's data, and default-deny egress with only required destinations allowed ✓ (correta)