Pass the Anthropic Claude Certified Architect CCAR-P Questions and answers with CertsForce

Adversarial tests directly evaluate behavior under attack, including prompt injection, malformed records, malicious document content, credential-extraction attempts, and unauthorized tool requests. Integration tests provide the complementary coverage required by the question: they verify authentication, retrieval, prompt construction, model invocation, validation, logging, and downstream actions as one end-to-end system. Regression, smoke, and unit testing remain valuable, but they do not jointly satisfy the two stated objectives. A stable regression set measures previously established behavior; smoke tests provide shallow availability confirmation; and prompt-rendering unit tests cover only one component. Anthropic recommends explicit prompt-injection defenses and task-specific evaluations that represent both normal and edge-case traffic. Prompt-injection mitigation

A project-scoped CLAUDE.md converts repeatedly supplied information into persistent, version-controlled context. Claude Code loads applicable CLAUDE.md instructions at session initialization, allowing architecture principles, coding conventions, build commands, repository structure, and verification requirements to be shared consistently across engineers. Anthropic explicitly recommends using CLAUDE.md for information that developers would otherwise explain repeatedly; concise and specific instructions provide stronger adherence. Committing the file also enables normal review and change-control practices. Option C reduces typing speed but preserves the underlying waste and inconsistency. Options A and D remove useful capability or governance rather than correcting the workflow. The implementation should periodically review the file for obsolete, conflicting, or unnecessarily verbose instructions.

Study Guide references/topics: Claude Code project memory ; CLAUDE.md; persistent context; repository conventions; workflow standardization.

Option D defines observable and repeatable escalation triggers. Confidence thresholds identify cases where the system lacks sufficient certainty. High-impact categories ensure that legally, financially, medically, or operationally consequential decisions receive human attention regardless of nominal confidence. Classifier-generated ambiguity flags add an independent control, while an explicit user request preserves the individual’s ability to obtain review. These triggers should be documented, tested, logged, and mapped to reviewer authority and response-time objectives. Options A and C make escalation discretionary and inconsistent. Option B is purely reactive because the potentially harmful automated action has already occurred. A sound workflow escalates before consequential execution and records the reason, evidence, reviewer, decision, and outcome.

Study Guide references/topics: Human-in-the-loop governance; escalation thresholds; ambiguity detection; consequential decisions; review auditability.

Option A augments the existing review process without transferring final accountability to the model. Claude Code can inspect the change in repository context, identify logic defects, security weaknesses, regressions, and maintainability concerns, and publish structured findings for human evaluation. Existing branch protection then preserves required approvals and separation of duties. Automatic merging or disabling protection rules would convert an advisory capability into an uncontrolled authorization mechanism. Silent deletion would eliminate transparency and prevent authors from challenging or correcting the assessment. Anthropic’s Code Review documentation states that findings are posted as comments and do not approve or block the pull request, leaving the established review workflow intact. Claude Code Review