Pass the Anthropic Claude Certified Architect CCAR-F Questions and answers with ExamsMirror

Direct @ references provide Claude with the exact implementations it must imitate. Anthropic documents that referencing a file with @ includes the full file content in the conversation, and multiple files can be referenced in one message. This gives Claude immediate access to the real transaction boundaries, exception structures, audit fields, naming conventions, and helper APIs used by the project. ( https://code.claude.com/docs/en/common-workflows )

Option B is inappropriate because the task is explicitly one-off and the conventions are already documented elsewhere. CLAUDE.md is loaded into every session and should contain concise information that broadly applies to the project. Adding detailed implementation material for a single integration would consume context unnecessarily. Anthropic recommends moving occasional procedures to skills and keeping CLAUDE.md limited to persistent, widely applicable guidance. ( https://code.claude.com/docs/en/memory )

Option C loses precision because a natural-language summary may omit subtle but important code behavior. Option D asks Claude to rediscover files that have already been identified, increasing exploration time and context usage.

The most effective prompt should reference all three modules, identify which pattern each demonstrates, specify the new module’s required behavior, and request focused tests proving that the established conventions were followed.

Official references/topics: @ file references, rich prompt context, CLAUDE.md scope, pattern-based implementation.

Option B preserves provenance as structured data throughout every handoff. Each finding should include the summarized claim and an associated source record containing its URL or document identifier, page or section location, relevant excerpt, and possibly retrieval date and confidence. The synthesis agent can combine claims without destroying this relationship, and the report generator can produce citations deterministically.

Anthropic’s multi-agent research system retains findings until a dedicated citation stage identifies the precise source locations supporting report claims. This demonstrates that citation provenance must remain available beyond initial retrieval and summarization.

Option A attempts to reconstruct provenance after it has been discarded, which is expensive and may associate a claim with a different or weaker source. Option C preserves evidence but wastes context by transferring approximately all raw search and document content. Option D is better than losing references entirely, but embedding citations only in prose makes programmatic validation, deduplication, and claim-to-source mapping less reliable. Structured content and source fields maintain both context efficiency and auditable attribution while allowing different agents to transform the narrative safely.

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Option A is the only configuration that establishes both required limits inside Claude Code. The official CLI reference defines --max-turns as the maximum number of agentic turns permitted in print mode; Claude Code exits with an error when that limit is reached. It defines --max-budget-usd as the maximum dollar expenditure on API calls before execution stops, including applicable subagent expenditure.

These controls address different failure dimensions. The turn limit prevents an investigation from continuing through excessive read-search-analyze cycles, while the budget limit stops an invocation whose expensive turns consume the monetary allowance before reaching the turn ceiling. Supplying both therefore creates an effective per-run boundary.

Option B controls how permission requests are handled; it does not limit the number of already permitted tool calls or API expenditure. Option C applies an external wall-clock timeout and merely observes cost, violating the requirement that Claude Code itself enforce both limits. Option D lowers expected cost per turn but creates no hard ceiling: the agent can still perform many iterations and exceed the intended budget. A cheaper model also does not guarantee that the review will terminate within a predictable number of turns.

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The pipeline must preserve source evidence while making inconsistencies explicit. Option D records the amount stated on the invoice separately from the total derived from extracted line items. A mismatch then becomes a machine-detectable validation condition rather than an invisible extraction defect.

This approach is superior because it does not silently overwrite source data or ask another model to guess which value is correct. Anthropic’s evaluation guidance recommends automated, code-based grading whenever the criterion can be expressed deterministically. Arithmetic reconciliation is precisely such a criterion. ( https://docs.anthropic.com/en/docs/build-with-claude/develop-tests ) In production, the summation should preferably be calculated by application code using normalized decimal values, even though the option describes the model populating calculated_total . The essential design principle remains the same: preserve stated_total , compute an independent total, compare them, and route discrepancies for adjudication.

Option A might improve behavior but cannot resolve genuine OCR corruption and could encourage the model to modify extracted values merely to create mathematical consistency. Option B introduces a second probabilistic judgment without new evidence. Option C is unacceptable for accounting data because it fabricates adjusted amounts and destroys fidelity to the invoice.

The schema should therefore expose both values and attach a validation status or review reason when they differ.

Official references/topics: Deterministic Validation; Human-in-the-Loop Review; Structured Output Design; Source-Fidelity Controls.

Forking is specifically designed for exploring alternative directions from a shared body of prior analysis. Each fork starts with a copy of yesterday’s conversation history, including the files read, architectural observations, dependency findings, and decisions already recorded. The microservice approach and the in-place refactoring approach can then develop independently under separate session IDs.

Anthropic’s Agent SDK documentation states that a fork creates a new session from a copy of the original history while leaving the original session unchanged. Each resulting session can subsequently be resumed independently. The documented implementation combines resume with fork_session=True in Python or forkSession: true in TypeScript. ( https://code.claude.com/docs/en/agent-sdk/sessions )

Option B allows conclusions, assumptions, and proposed edits from the first approach to contaminate the evaluation of the second. Option C preserves context for only one branch and forces the engineer to reconstruct context manually for the other. Option D discards the detailed analysis already captured in the session and depends on potentially incomplete summaries.

Two forks provide equivalent starting conditions, preserve the parent investigation, and support a fair comparison of scope, migration risk, operational complexity, and required code changes. Filesystem edits should still be isolated through worktrees or checkpointing because session forking branches conversation history, not the working directory.

Official references/topics: Agent SDK Sessions; Session Forking; Alternative-Approach Exploration; Context Preservation.

Option C allows orchestration effort to scale according to the actual evidence and processing requirements of each request. A factual lookup might require one search operation and a direct coordinator response, while comparative research may justify parallel search, document analysis, synthesis, and formal report generation.

Anthropic’s orchestrator-workers guidance describes a central model dynamically decomposing a task, choosing workers, and combining their results. This pattern is particularly appropriate when required subtasks cannot be predicted reliably in advance. Anthropic’s multi-agent research architecture similarly uses a lead agent to analyze the query and determine an appropriate research strategy.

Option A creates an inflexible two-route system and still sends every non-factual query through the most expensive workflow. Option B requires labeled training data, an externally maintained classifier, and a stable definition of the optimal agent combination. Option D is less expensive to implement but becomes brittle as query types evolve beyond predetermined categories. Dynamic coordination uses the model’s existing reasoning ability to assess source requirements, analytical depth, expected output, and uncertainty before invoking only the specialists that materially improve the result.

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Option B teaches Claude the actual decision boundaries behind the team’s judgments. Contrasting examples can demonstrate why one apparent security pattern is acceptable within the organization’s deployment controls while a superficially similar implementation is genuinely unsafe. The same technique can distinguish established style conventions from deviations and intentional performance trade-offs from accidental inefficiencies.

Anthropic identifies relevant, diverse, and clearly structured examples as one of the most reliable ways to improve Claude’s accuracy and consistency. Its official prompting guidance recommends examples that closely mirror the production task while covering important variations and edge cases. This encourages generalization based on demonstrated criteria instead of memorization of prohibited wording.

Option A can suppress legitimate findings merely because they contain a selected keyword. Option C will become lengthy, difficult to maintain, and incapable of enumerating every future code variation. Option D is too vague and may reduce recall by discouraging Claude from reporting uncertain but genuine defects. Few-shot contrasts provide precise, maintainable behavioral guidance without imposing a blanket suppression policy.

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Option B uses the tool designed to search file contents. Anthropic’s Claude Code tools reference distinguishes Grep from Glob: Grep searches lines inside files, whereas Glob matches filenames and paths. Grep is built on ripgrep and accepts regular-expression patterns, so the opening parenthesis should be escaped as eval\( when the intention is to match the literal function call. The search can return matching files, line numbers, and surrounding context without loading every file into the model’s context window. Option A searches filenames resembling eval , not source files whose contents invoke the function. Option C follows only code reachable from a selected entry point and can miss test utilities, dynamically loaded modules, scripts, and dormant vulnerable code. Option D combines a recursive filename listing with text filtering; it still searches names rather than file contents. After Grep identifies matches, the agent should use Read on the relevant ranges to distinguish actual executable calls from comments, strings, or safe wrappers. Grep therefore provides the most complete and context-efficient initial security scan.

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Option A uses the dedicated configuration mechanism for Anthropic’s managed Code Review service. The official Code Review documentation states that a root-level REVIEW.md is injected into every review agent as the highest-priority instruction block. It can redefine severity, suppress categories already enforced by CI, skip generated paths, and require concrete evidence before particular findings are reported.

The proposed rules map directly to the observed noise. Skipping lint and formatting findings eliminates duplication with deterministic CI checks. Excluding src/gen/* prevents comments on machine-generated code. Requiring a specific source line demonstrating incorrect behavior raises the verification threshold for rendering-helper findings without suppressing genuine defects.

Options B and C configure self-hosted GitHub Actions workflows, but the scenario concerns managed Code Review running through the Claude GitHub App on Anthropic’s infrastructure. Those workflow prompts do not govern the managed reviewer. Option D supplies useful general project context, but CLAUDE.md applies to all Claude Code tasks and has lower review-specific authority. The documentation states that REVIEW.md is the stronger control for changing what managed Code Review flags and how findings are reported. It therefore provides the most targeted solution with the least unnecessary infrastructure.

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MCP prompts are exposed as user-invoked commands rather than autonomous tools or permanently loaded system instructions. Claude Code dynamically discovers prompts from connected MCP servers and displays them in the command list using the naming convention /mcp__servername__promptname .

Arguments are supplied as space-separated values after the command. When executed, the MCP server resolves the prompt and its returned content is injected into the active conversation. Anthropic’s official documentation provides examples such as /mcp__github__list_prs and /mcp__jira__create_issue " Bug in login flow " high . ( https://code.claude.com/docs/en/mcp )

Option A would consume context continuously and incorrectly treat optional workflow templates as mandatory system instructions. Option B confuses MCP prompts with MCP tools: tools are model-callable operations, while prompts are reusable prompt templates invoked as commands. Option C describes MCP resources, which can be referenced and attached but are a distinct MCP capability.

For the stated server, the team could invoke commands such as /mcp__devops__deploy_checklist or /mcp__devops__incident_response service-name . The exact server segment is derived from the configured server name, with normalization applied where necessary.

Official references/topics: MCP Prompts; Dynamic Prompt Discovery; MCP Slash-Command Naming; Prompt Arguments.