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

Option C identifies the architectural contradiction. A specialized subagent’s role prompt says what work it should perform, but granting it every tool simultaneously signals that unrelated actions are available and potentially legitimate. The synthesis agent can therefore interpret web search as a permissible way to fill gaps, while the report generator may invoke document-analysis tools even though those responsibilities belong elsewhere. Anthropic’s Agent SDK documentation describes subagents as focused workers with tailored instructions and explicitly supports restricting each subagent to specific tools; it notes that tool restrictions reduce unintended actions. There is no documented universal threshold at which choosing among 18 tools becomes unreliable, so option B invents a “four or five tools” limit. Context consumption in option A may affect efficiency, but it does not directly explain the role-specific cross-calling pattern. Option D is also incorrect because the observed behavior occurs after delegation, inside agents that already possess the tools. The remedy is least-capability configuration: give the web agent search tools, the document agent document tools, the synthesis agent only the inputs and utilities required to combine findings, and the report agent formatting or file-output tools. Aligning role, description, and tool allowlist removes the conflicting affordances.

================

The search must first establish the function’s complete naming surface. Reading the core definition and every wrapper or re-export reveals aliases such as computeOrderTax , names introduced through intermediate modules, and potentially different import paths. Once those names are known, the agent can search the entire codebase for each exposed identifier and inspect the resulting call sites.

Claude Code’s Grep tool searches patterns inside file contents, whereas Read provides the surrounding implementation needed to determine whether a wrapper delegates to, renames, or conditionally invokes the original function. These tools are complementary: Read establishes semantic identity; Grep provides broad reference discovery. ( https://code.claude.com/docs/en/tools-reference?utm_source=chatgpt.com )

Option B misses every caller using a wrapper alias. Option A may find importing files, but import-level discovery is indirect and can miss re-exports, namespace imports, dependency injection, or calls made through a locally renamed symbol. It also requires unnecessary manual inspection of every consumer of the module. Option D relies on documentation, which may be incomplete or stale and cannot prove that all executable references have been found.

Before removal, the agent should also inspect tests, dynamic registrations, configuration-driven references, and generated code where relevant. The defining requirement is to map the alias chain first and then search all discovered public names.

Official references/topics: Read and Grep Tool Behavior; Symbol and Alias Discovery; Codebase Reference Tracing.

The problem is an undefined relevance threshold. Claude is finding technically valid observations, but the prompt does not clearly distinguish actionable defects from acceptable conventions and low-value style preferences. Option A establishes explicit positive and negative reporting criteria, allowing the model to apply the team’s actual definition of a useful finding during generation.

Anthropic’s prompt-engineering guidance emphasizes clear, specific instructions and well-defined success criteria. The managed Code Review documentation similarly recommends defining skipped categories, generated paths, severity rules, and evidence requirements. Reporting “bugs affecting correctness or security” while excluding “formatting handled by CI and documented local conventions” is substantially more precise than asking the model to be generally conservative.

Option B adds cost, latency, another probabilistic decision, and a new evaluation surface before improving the original prompt. Option C confuses confidence with importance: Claude may be highly confident about a trivial style observation. Option D is dangerously broad because it can suppress legitimate but uncertain bugs, reducing recall. Explicit relevance criteria address the demonstrated failure directly while preserving the model’s ability to investigate and report genuine correctness or security problems.

================

Option C creates an objective verification loop. The tests encode expected traversal behavior for disconnected graphs, cycle handling, weighted edges, invalid inputs, and performance constraints. Claude can implement the algorithm, execute the suite, inspect concrete failures, and refine the implementation until the measurable conditions pass.

Anthropic emphasizes giving Claude a verification mechanism such as tests, builds, linters, or fixture comparisons. Without an executable pass-or-fail check, Claude can only determine that an implementation appears complete. With tests, it can perform work, evaluate the result, and iterate using evidence rather than subjective judgment. Anthropic also recommends reproducing defects with failing tests before applying corrections. ( https://code.claude.com/docs/en/best-practices )

Option A may produce a thoughtful initial design but does not guarantee progressive improvement after implementation. Option B risks inheriting assumptions or deficiencies from a reference that may not match the project’s constraints. Option D depends on manual review and converts the developer into the primary verification system.

The test suite should include correctness fixtures, boundary cases, complexity-sensitive workloads, and regression tests added whenever a new failure is discovered. This makes every iteration cumulative: a correction must satisfy the new case without breaking previously validated behavior.

Official references/topics: Executable Verification; Test-Driven Iteration; Feedback Loops; Regression Testing.

Option C gives the reviewer access to the evidence currently missing from its prompt. An agentic review can use Grep, Glob, Read, language-server tools, and test commands to locate callers, follow imports, inspect type definitions, and verify whether a suspected compatibility issue actually exists. A turn limit controls cost while still allowing targeted exploration.

Anthropic’s context-engineering guidance recommends just-in-time retrieval: agents should retain lightweight references and dynamically load the information needed for the current task instead of preloading a large fixed context. Anthropic has also reported that code-review performance improves when the necessary repositories are available for gathering complete context. Option A may load many irrelevant files while still missing reflection, generated interfaces, dependency injection, or indirect call paths beyond the arbitrary two-hop boundary. Option B cannot reason reliably about code that the model cannot inspect. Option D improves coverage but fragments repository-wide relationships and may duplicate substantial work. An agentic loop can begin with the diff, identify uncertain cross-file effects, search only relevant paths, and report findings only after locating supporting code. Anthropic context-engineering guidance , Claude Code quality report

================

A mandatory compliance threshold must be enforced outside probabilistic model reasoning. A PreToolUse hook can inspect every attempted process_refund call before execution, compare its amount with the $500 threshold, and deny the call when the limit is exceeded. The host application can then create the human-escalation case using the validated customer and order context.

Anthropic describes hooks as deterministic controls that ensure required actions occur instead of relying on the model to choose them. Agent SDK documentation confirms that a single denying PreToolUse hook blocks the tool call, including MCP tools matched through their qualified tool names. ( https://docs.anthropic.com/en/docs/claude-code/hooks-guide )

Options A and B improve expected behavior but cannot guarantee compliance. The stated production failure rate demonstrates that prompting alone is insufficient. Option C prevents the refund from completing, but the subsequent escalation still depends on Claude correctly interpreting the returned error and invoking the human tool. It therefore enforces the financial block but not the complete escalation requirement.

Option D enforces both sides of the policy at the orchestration boundary: deny the unauthorized action and route the case to an approved human process. The escalation action should be idempotent and auditable.

Official references/topics: PreToolUse hooks, deterministic policy enforcement, MCP tool interception, human-approval controls.

Option B follows the Claude Agent SDK’s default context-isolation model. A newly spawned subagent receives a fresh context window and does not automatically inherit the parent’s conversation history or previous tool results. The coordinator must therefore include the information required for synthesis directly in the spawning prompt, preferably using clearly separated structured sections for web findings, document findings, source metadata, conflicts, and unresolved questions.

Anthropic’s official SDK subagent documentation states that the Agent tool’s prompt string is the content passed from the parent to the new subagent. It specifically advises including required file paths, errors, and decisions in that prompt because parent context is not inherited.

Option A could work only if the application had deliberately implemented and authorized such a shared-memory architecture. The question establishes no such mechanism, and reference identifiers alone provide no evidence to the subagent. Option C introduces a callback protocol that is unnecessary for a normal handoff. Option D is incorrect because subagent isolation expressly prevents automatic inheritance. “Complete findings” means the complete evidence needed for synthesis, not every intermediate search trace or irrelevant tool result.

================

Retry-with-error-feedback is effective when the required information is available and the defect concerns representation. Options A, C, and D are correctable formatting failures: the model can flatten an object into an array, remove thousands separators and emit an integer, or convert a datetime into the required date-only format. The validation message supplies enough information to revise the output.

Option B is fundamentally different. The complete co-author list is absent from the model’s input and exists only in an external document. No number of retries can recover evidence that was never supplied. Repeated attempts may instead increase the probability of fabrication. Anthropic’s hallucination guidance recommends restricting responses to available documents, allowing the model to acknowledge missing information, and withholding or retracting claims that cannot be grounded in the source. ( https://docs.anthropic.com/en/docs/test-and-evaluate/strengthen-guardrails/reduce-hallucinations )

The correct response is therefore to return an explicit missing-data state, retrieve the external document through a tool, or route the record for enrichment. Retrying without changing the information boundary cannot solve the problem.

Current Anthropic Structured Outputs can eliminate many shape-related failures by guaranteeing JSON Schema conformance, but they still cannot create unavailable source facts. ( https://platform.claude.com/docs/en/build-with-claude/structured-outputs ) This distinction—format failure versus evidence failure—is central to reliable extraction architecture.

Official references/topics: Retry Boundaries; Missing Context; Grounded Extraction; Structured Outputs; External Data Retrieval.

Option D separates durable case history from volatile operational data. The new session receives a compact, structured summary describing the billing dispute, the customer’s objective, actions previously taken, and the unresolved status. It does not inherit outdated backend observations as though they were still authoritative. Fresh tool calls then retrieve the current refund or order state before the agent responds.

Agent SDK sessions preserve conversation history, including earlier tool calls and tool results. Resuming the complete transcript therefore reintroduces stale system data into the active context, even though the external backend may have changed substantially during the four-hour gap. Conversation persistence must not be confused with persistence of external-system truth.

Option A performs unnecessary calls to every previously used tool, including tools unrelated to the returning customer’s current question. Option B relies on prompt compliance while retaining contradictory historical evidence in context. Option C manually removes tool results from an existing transcript and may damage the logical relationship between prior tool_use and tool_result blocks while still retaining a long, unstructured conversation.

A structured summary should preserve stable identifiers, previous actions, customer commitments, and unresolved issues. Time-sensitive fields such as refund status, delivery state, account balance, or expected resolution should always be refreshed through authoritative tools.

Official references/topics: Session persistence, stale tool-result management, context compaction, fresh-data retrieval.

Option C applies the two native stopping controls required by the question. Anthropic’s Claude Code CLI reference defines --max-turns as the maximum number of agentic turns permitted in print mode and --max-budget-usd as the maximum API expenditure for that invocation. When the turn limit is reached, the run exits with an error. Spending by subagents counts toward the budget cap, and current Claude Code versions stop remaining background subagents when the limit is reached. These flags therefore control the complete invocation rather than merely one model request.

Option A lowers expected cost per iteration but establishes no hard ceiling on either iterations or total expenditure. Option B limits wall-clock execution at the CI-runner level and only observes spending retrospectively; it does not enforce a Claude Code budget. Option D controls permission behavior, not iteration count, execution time, or token spending. The pipeline should use both flags, capture the resulting nonzero exit or structured error status, and report whether a review ended normally or was terminated by a configured operational limit.

================